Reconciles ChatGPT's four independently-branched extraction PRs (#451/#452/
#453/#454) into one coherent generate/derivation/extract.py. They each rewrote
the same file + same new test off main, so they conflicted pairwise and needed
integration, not a merge.
Integrated (span-tracked, most-specific-pass-first so numbers are never double
counted):
- EX-1 word-numbers (#452): reuses WORD_NUMBERS; tens-one hyphen compounds;
factor-bearing half/third/quarter excluded.
- EX-4 list-unit inheritance (#451): bare numeric list with one trailing unit.
- EX-5 sentence-final numbers (#454): bare final number with empty unit.
Deferred: EX-3 multi-word units (#453). Its greedy lowercase span reads
"6 apples and 4 apples" as unit "apples and", regressing GB-2's
test_same_unit_list_sums, and still can't recover real multi-word units from
0024-class text ("jumping jacks on"). Needs a tighter rule; see
docs/handoff/AUDIT-ADR-0179-EX-RECONCILE.md.
Verification (sealed lane only; chat/ does not import this module):
- Serving frozen: lane-SHA 8/8 match, generate_claims --check OK -> 3/47/0
byte-identical, wrong=0 held.
- Sealed practice improved 4/2/44 -> 4/1/45: case 0025 flips wrong->refused.
EX-1 reads "three", so completeness sees a quantity the 6x50 chain omits and
refuses the spurious 300 (gold 1200) instead of committing it.
- No new test failures (3 pre-existing on main).
Also fixes stale test drift from EX-2 (#447): TestDecimalGroundingGapIsDeferred
asserted decimals still refuse, but #447 made $0.75-class resolve to 864.
Renamed to TestDecimalGroundingResolves and updated to assert the flip.
Honest scope note: EX-4 does NOT unblock real case 0024 (its PR test used a
fabricated bare-list paraphrase). TestRealCase0024StillBlocked pins the true
boundary.
ADR-0175 Phase 3b — the first live attempt generator. Runs only in the sealed
practice lane, only on cases the engine refused; every proposal is gated by the
Phase 3a self-verification gate.
generate/derivation/:
- extract.py: extract_quantities() — lexeme-level (number + unit word; ADR-0165).
- search.py: search_multiplicative() — one in-clause product candidate per
sentence with >=2 quantities + a present multiplicative cue; gated by
select_self_verified. Per-sentence scope + multi-candidate disagreement give
the uniqueness gate real teeth (two qualifying sentences -> refuse). The cue
set {each,every,for,per,times} is an explicit PROVISIONAL hypothesis the
practice loop refines, not a claimed-correct grammar.
evals/gsm8k_math/practice/v1/search_runner.py: search_augmented_scorer +
build_search_report — base scorer, then a practice-only attempt on refusals.
MEASUREMENT (the deliverable, per the breadth-of-impact test):
practice with search: correct=4 wrong=9 refused=37 (baseline 3/0/47)
- Flips +1 (0021, the clean in-clause aggregate) and its renumbered/reworded
variants (ADR-0114a perturbation guard) -> a real capability, not memorisation.
- 9 wrong attempts -> elimination records (§9), the learning signal. The naive
full-product cue model over-attempts; the eliminations are exactly the signal
that refines it.
HONEST FINDING: self-verification (grounding ∧ cue ∧ unit ∧ uniqueness) is
NECESSARY but NOT SUFFICIENT — 9/13 self-verified attempts were wrong vs gold.
The gap is cue PRECISION / which-quantities-compose (the knowledge axis), not
'can we multiply' (skill). This is why the search runs sealed: gold catches the
9, and case 0050 (canary) attempted-and-failed IN PRACTICE without touching
serving -> validates the seal.
Invariants: #1 seal (serving still 3/47/0; 0050 refuses in serving; no
generate/chat import of the lane), #3 determinism. Serving wrong=0 untouched.
Verified: 3a+3b 31/31; ruff clean; serving lane 4/4; smoke 67/67.
