The "before/left" reading-rule lever. The microscope showed only the question-time
reading is cleanly achievable: "for N money = spend" is cue-precision-blocked (the
`for` cue is overloaded across train_sample -- `for $2`, `for 14 days`, `for 10 reps`
-- so a spend rule risks regressing train-0021/0003 and is the overfitting trap),
and the disguised-polarity cases already refuse via pooling. "left" is already handled
by loss verbs.
What ships: a question-scope guard. A question asking for a state *before* a stated
change ("How much did Lisa have before lunch?", gold 50) asks for a temporal point
the forward composers do not compute -- they derive the final/net state (50-20=30).
Until a question-time reader exists that is a refusal, never a guess at the wrong
point. target.asks_prior_state detects before/initially/originally/at first/to begin
with/to start with/at the start in the QUESTION CLAUSE only (the last `?`-sentence),
so body narrative does not trip it -- verified safe against train-0003 ("sells before
school starts"), 0010 ("had 20 initially, then lost 12"), 0028. `used to` is excluded
(the purpose infinitive "beads used to make a bracelet" is a false positive).
resolve_pooled refuses when asks_prior_state holds.
Result (sealed lane; chat/ does not import these -> serving 3/47/0 frozen):
- confuser wrong 2 -> 1 (only 0016 distractor-anchor-skip remains). temporal-scope
category cleared (0 wrong / 2 refused). pair-tells 1 -> 0: 0020 ("before lunch")
refuses while its minimal-pair twin 0021 ("left", gold 30) still solves the net --
the discrimination the corpus is built to measure.
- genuine positives still 7 solved, 0 wrong.
- train_sample 3/47/0 and practice 3/47/0 byte-identical.
- 205 derivation/target/pool tests + 40 architectural invariants green.
Tests:
- test_adr_0182_pool.py TestPriorStateQuestionGuard: detector true/false edges
(before-in-question vs before-in-body vs `used to make` false positive vs the
`left` twin) + resolve_pooled refuses the before-question while the left-twin
resolves forward to 30.
- test_adr_0163_f2_confusers.py: baseline wrong 2->1, pair_tells 1->0; new
test_temporal_scope_does_not_misfire + test_before_left_minimal_pair_discriminated.
Stacked on #476 (pooling); merge #476 first. 0016 anchor-skip is the next lever.
Pairs with ADR-0163-F2 graduation amendment (#478).
MS-1 of multi-step composition. Turns the question into a Target = what the
problem asks for, the search's pruning signal + stopping criterion (MS-3).
Lexeme-level only (ADR-0165): the existing question parser returns nothing on
these GSM8K questions, and 0165 forbids new question-shape grammar regex. Three
robust signals:
- quantities: numbers stated IN the question (0033's 'when she is 25') via the
body's lexeme extractor — they participate in the derivation.
- aggregation: presence of an aggregation lexeme (total/altogether/combined/sum/
'in all'/'in total') — soft hint the final step is a sum.
- units: asked units resolved by INTERSECTION with the body's known units
(precise lexeme match, e.g. 'jumping'). Superordinates (weight<->pounds) are
NOT faked — deferred to a curated superordinate-units pack; until then the unit
signal is precise-but-incomplete and the search leans on completeness.
Refuse-preferring: empty target field is not an error, just a weaker prune.
generate/derivation/target.py: Target + extract_target(question, known_units=()).
12 MS-1 tests (question-quantity, aggregation, body-unit intersection,
superordinate-not-faked, determinism, frozen). Verified: derivation suite 57/57;
ruff clean; smoke 67. Not wired into serving (Target ready for MS-2/MS-3).
