ForcedChoiceResult now carries pmass_format (sum prob mass on the K
foundation answer tokens at the JSON answer slot, averaged across fwd
and rev framings). eval.py aggregates it as mean_pmass_format in both
the headline return dict and the info subdict, and propagates per-row
for sweep/audit consumers.
Direct coherence canary: drops when steering pushes the model toward
non-foundation tokens (gibberish, refusal, format collapse). Independent
of which foundation is picked — complementary to top1_acc (label-
agreement; intentional target shift) and mean_nll_prompt (teacher-forced
prompt nll; falls under steering even when generations break).
Surfacing this lets downstream callers (weight-steering-lite walkback,
report dashboards) gate on actual coherence rather than misusing top1
as a budget.
The 1-row demo block (prompt + think + nudge + prefill + scored token +
64-token free continuation) used logger.info, which meant any caller
wrapping the function in an INFO-level sink (e.g. an agent harness)
got the full trace in their stdout. Downgrade to logger.debug so it
still lands in the user's verbose log but doesn't leak to agents.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replace 3 parallel scoring paths (guided_rollout / _batch / _multibool)
with a single internal `_rollout_kv_fork` core: phase-1 batched think,
one cached prefix forward, N forked suffix forwards (one per scoring
slot). Binary case is just N_slots=1.
Drops from GuidedResult (no callers): answer_text, raw_full_text,
rep_ratio_think, prompt_nll. eval.py updated accordingly. _ngram_rep_ratio
and _scoring_text helpers removed -- their logic folded into the core.
Verbose=True now logs the full conversation (prefix + suffix the model
sees) plus a 64-token free-form generate continuation, so format issues
are obvious from one slot's log.
File shrinks from ~600 to ~340 lines. smoke_batch_parity passes (bf16
max Δp_true=0.098 within 0.20 tol; pre-existing batched-greedy drift).
Replace mid-turn splice (`I should answer now.</think>{prefill}`) with a
clean turn close + user nudge + fresh assistant prefill. Mirrors what a
chat UI emits when a human interrupts a partial assistant turn, which is
on-policy in chat-tuned training data. Empirically: pmass_format ~0.987
on smoke set vs the OOD splice path.
Close marker is probed from the tokenizer's chat template (sentinel
diff), so it works on Qwen/ChatML, Llama3 (`<|eot_id|>`), etc -- no
hardcoded `<|im_end|>`.
Drops:
- emitted_prefill field (no callers)
- try/except TypeError around apply_chat_template (defensive)
- enable_thinking=False kwarg (some templates reject it; complete
assistant messages auto-strip the think block anyway)
- `\nI should answer now.` fallback in multibool
Adds:
- verbose=True flag on guided_rollout to log scoring_text for debugging
- _assistant_close(tok) sentinel-probe helper
Note: prompt_nll magnitudes shift since scoring_text now includes the
user-nudge tokens. Not comparable to pre-refactor saved results.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- suf_ids_for() now uses interrupt-msg format: close assistant turn, inject
per-foundation user question + {"Answer": prefill -- fixes authority pmass
(0.344->0.898) by avoiding JSON string-priming from key names
- Add _FOUNDATION_DESCS and _DEFAULT_MULTIBOOL_HINT rubric for discrimination
- Low-pmass diagnostic: first occurrence now runs .generate(max_new_tokens=32)
to show what model actually produces; subsequent cases log top-5 tokens
- suf_ids_per stored so diagnostic generate can reconstruct full input
- Journal: add inter-foundation correlation results (mean |r|=0.51); note
Spearman vs human raters is not a valid metric (exclusive vs independent labels)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The chained-fill design (one suffix with all foundations, two passes for true/false)
hit a non-recoverable conv-state issue on hybrid linear-attention layers (Qwen3.5):
splitting prefix and suffix forwards via past_key_values silently produced
wrong logits (pmass dropping to 0.04, top token leaking to ' "' = 0.72).
Switched to 12 independent single-slot completions per prompt:
for (frame, foundation) in {is_violation, is_ok} × foundations:
cache scoring_prefix once, fork suffix `\n{"<frame>": {"<f>":`,
read logits at the last token (predicting `true|false`).
final[f] = 0.5 * (lr_violation[f] - lr_ok[f])
Framing flip cancels per-key prior bias the same way true/false fill did,
without the chained-slot causality that interacts badly with split forwards.
Added _assert_full_attention(): checks model.config.layer_types and fails
loudly on hybrid models. Verified parity vs flat forward on Qwen3-0.6B
(Δ ≤ 0.13 nats; signal of interest is ≫1 nat) and assert fires on Qwen3.5-0.8B.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-token NLL over the scoring text is free since we already compute
full-sequence logits in guided_rollout / guided_rollout_batch (just
gather instead of slicing [:, -1]). Higher NLL = model less coherent
on this prompt under whatever steering is attached.
eval.py logs pmass/ppl/nll aggregate at end of guided eval so the
next run shows degradation at a glance instead of buried tqdm noise.
analyse() now exposes raw_nll and info.prompt_nll_mean.
`boolean` is not valid JSON; use `{"type": "boolean"}` so models
produce true/false rather than the integer shorthand 1.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
`boolean` is not valid JSON; use `{"type": "boolean"}` so models
produce true/false rather than the integer shorthand 1.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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
Was emitting `logger.warning("pmass=0.XX<0.9 — top-5: ...")` per-row, which
spammed the log heavily during heavy-steering eval (many rows go OOD at once).
Now collects all low-pmass rows in the batch and emits one summary line with
the worst-case top-5, e.g.:
pmass<0.9 on 7/16 rows in this batch; worst=0.412 top-5: '1'=0.40, ...
Same diagnostic signal, ~16× fewer log lines per batch.
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.
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