nll_prompt was removed from per_row in f585864 but the script still read it,
crashing the smoke test. Remove the dead refs and surface the new
informedness scalar alongside top1_acc.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Sanity check + baseline before wiring multibool into the steering sweep.
Computes per-foundation logratios on all 132 classic vignettes, dumps to
data/results/multibool_baseline.jsonl, prints per-foundation lr summary
and Spearman corr against human-rater %s.
SHOULD: mean pmass > 0.9; Spearman ρ > 0.3 on ≥4/6 foundations.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Updated scripts/07a_merge_labels.py to only merge calibrated_* and llm_dominant
- Updated README.md and scripts/05_upload_hf.py to reflect removed columns
- Re-merged and re-uploaded clean datasets to HuggingFace
- Add scripts/07_multilabel.py: LLM judge rates all 7 foundations per vignette
using violation (forward) and acceptability (reverse) frames
- Foundation definitions drawn from Clifford et al. (2015) survey rubric
- Z-score each frame per foundation before averaging to cancel range bias
- Calibrate LLM Likert → human % via per-foundation OLS (classic set only)
- Add scripts/07a_merge_labels.py: merges llm_* and calibrated_* into vignette files
- Update README and HF dataset card with methodology and calibration quality table
- Classic set: 80.3% dominant-foundation accuracy, Pearson r 0.69-0.89 per foundation
Sequential eval was the bottleneck (~12s/vignette × 131 = 27 min/pass; with
bidirectional ±C × 14 methods, that projected to ~17 h). Three model calls
per row (phase1 generate, scoring forward, cosmetic continuation) became one
phase1 + one scoring per *batch*; continuation generate dropped (callers
only use p_true + pmass_format).
Parity smoke (scripts/smoke_batch_parity.py): float32 is bit-exact (max
Δp_true=0.0000 over 16 prompts, 4.3× speedup at limit=4). bf16 drifts on
individual rows — greedy argmax flips at near-tie tokens then phase1
diverges — but pmass agrees within 0.03 (scoring forward correct) and
aggregates over 131 vignettes will average out the per-row noise.
Other changes shipped in this commit:
- core.py: analyse() now returns raw_pmass dict alongside raw p_true (callers
needed per-(vid,cond,frame) pmass for diagnostic warnings).
- guided.py guided_rollout: warn + log top-5 when pmass<0.9 (catches OOD
steering / format-broken vignettes without a separate audit pass).
- eval.py: pre-tokenize a sample to log expected prompt+cache budget so OOM
is predictable from the SHOULD line; group items by frame so each batch
shares schema_hint + prefill.
- Refactored evaluation logic in `src/tinymfv/eval.py` to support a new `max_think_tokens` parameter, allowing for a fixed continuation budget before scoring.
- Introduced `guided_rollout` function in `src/tinymfv/guided.py` to handle the generation of multiple tokens and scoring based on a deterministic continuation.
- Updated the CLI in `scripts/03_eval.py` to accept `--max-think-tokens` argument for controlling the token budget during evaluation.
- Created a new specification document `docs/spec/20260501_n_token_eval.md` outlining the goals, requirements, and tasks for the N-token evaluation feature.
- Simplified the record creation in `scripts/02_rewrite.py` by extracting logic into a new `make_rec` function for better code organization.
other_violate is now the verbatim source (no LLM), only self_violate is
rewritten. For airisk specifically, the AI-as-actor framing is preserved
in 1st-person ("You, an AI X bot, ...") since a naive "You ..." rewrite
silently swaps the actor archetype to human (16/132 flagged by judge).
Pairwise consistency check (06_consistency.py) verifies same-situation
across the perspective shift. After the fix:
- clifford 97.7% same
- scifi 99.2% same
- airisk 86.3% -> 100.0% same
First eval signal on Qwen3-0.6B: airisk wrongness=+0.70, gap=+0.43 vs
clifford/scifi ~0; model recognizes AI misbehavior as wrong but is much
more lenient when prompted as the AI itself.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
