administer()/read_items() now route through _rollout_natural_or_forced (the
nominal MFV core) instead of a think=0 single forward, so an activation steer
accrues over the think trace before the prefilled answer slot is read (spec
moral_aliens_engine.md, resolved decision: ordinal needs a think budget). The
only per-instrument difference is the answer-token set + the downstream reducer.
force_only on the shared core: the ordinal "(" prefill is one common char, so
natural-emission detection would match it by chance in the think trace and read
logits mid-think; surveys always force-read the answer slot. Nominal path keeps
natural emission (force_only defaults False). max_think_tokens floor is 1.
Smoke (tiny-random): ordinal + nominal both run; force-only demo reads the
forced ( slot.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The prior commit "fixed" p_a/pmass into a softmax to avoid NaN at coherence collapse.
That was wrong for this codebase: renormalizing within allowed tokens discards the mass,
so a distribution built from ~zero mass and one from real mass look equally comparable
after renorm -- they are not (the mean of 10 != the mean of 130). p/pmass -> NaN at
collapse poisons that item's factor, which is the honest "do not compare" signal, not a
bug. softmax was a silent fallback fabricating a comparable-looking number from incoherent
output. Restored, with a comment so it does not get re-"fixed". (Over-trusted the external
reviewer here against the repo's own no-defensive-programming rule.)
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Second external review (gpt-5.5, correctness-focused) on the post-cleanup tree.
Found no off-by-one/double-flip in the ordinal canonicalization+keying. Fixed the
parts I agreed with and could verify on the path the experiment uses:
- read.py: NaN-safe answer-token renorm. p_a/pmass poisons the profile with NaN when
pmass underflows to 0 at coherence collapse -- exactly when pmass should just flag it.
softmax(logp_allowed) is identical when pmass>0 and stable at collapse.
- maps.ipsative_pca: move SVD sign-stabilization INTO the helper so it and
plot_ipsative_pca share one orientation (saved coords could otherwise mirror the figure).
- instrument: assert ordinal answer_space is ['1'..scale_max] IN ORDER (reduce_ordinal
weights by position; a reordered space silently inverts E) -- was length-only.
- instrument.per_item_categorical: assert per-item dimension/sign agree across frames and
frames are distinct, instead of silently averaging under rows[0]'s metadata.
- pyproject: move matplotlib+textalloc to an optional `maps` extra; evals stay headless.
- tests: drop imports of the deleted reduce_nominal/expected_value, inline the expectation,
remove the now-impossible nominal-reducer test.
Deferred (flagged to maintainer): two NaN/window issues in guided.py's forced-choice
rollout (nominal evaluate() path) -- not exercised by this experiment, can't smoke-test,
and the NaN-as-collapse-signal there is a deliberate design.
Verified: experiment smoke green on all 4 instruments (no assert false-fires), 6 pure
unit tests pass, headless import clean, 16pf map renders.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Whole-library review (deepseek-v4-pro) flagged tinymfv as not-yet-ready as a shared
dep. Fixes for the parts I agreed with:
- lazy `maps` import via module __getattr__ so `import tinymfv` stays headless/fast
(no forced matplotlib) for numeric-only consumers; `tinymfv.maps.*` still works.
- trim __all__ to the front door (entrypoints + types + data api); plumbing stays
importable but out of `import *`.
- delete dead code: reduce_nominal + REDUCERS (evaluate folds its profile inline),
expected_value, HF_REPO, ROOT, _DEFAULT_FORCED_HINT.
- type administer's return as a TypedDict (AdministerResult/ItemRow/ItemFrameRow) so
the schema is documented + checkable without reading source; still a plain dict at
runtime (zero consumer churn).
- maps.plot_ipsative_pca: parametrize the legend labels (defaults preserve output)
and rename hon/dis -> pos/neg so a non-honesty steer gets a correct legend.
- drop 'canary' jargon and panel/review-# archaeology from comments.
Verified: `import tinymfv` no longer loads matplotlib; lazy maps still resolves;
experiment mfq2 smoke green through the typed administer.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Port the answer-token survey readout from the weight_steer_honesty experiment
(mft_honesty.admin) onto the instrument.py canonicalize-at-reader design:
- read.py: generalized answer-token reader (any answer_space + prefill)
- administer.py: read all frames -> per_item_categorical -> reduce_ordinal -> profile
- instruments.py: build MFQ-2/Big5/16PF/HSQ Instruments from bundled survey JSONs
- instrument.py: add display + human_csv fields for the map layer
- data/: survey JSONs + human country CSVs (lean: no raw survey responses)
Parity: experiment's parity_administer_check.py shows max per-foundation diff
8.25e-08 vs admin.administer on the tiny model (same function of same logits).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
