MS-3 composes MS-1 (Target) + MS-2 (comparative chains + completeness) + the gate.
generate/derivation/multistep.py: search_chain(problem_text, target=None).
Shape-based, NOT blind enumeration: enumerates a small principled candidate set
(product-of-all if a multiplicative cue is present; sum-of-all if an aggregation
hint is present; each optionally + comparative scalars), each using all
quantities, routed through select_self_verified (grounding ∧ cue ∧ unit ∧
completeness ∧ uniqueness). Bounded (MAX_QUANTITIES, refuse-on-overflow) +
deterministic. Target supplies the aggregation cue + question quantities; target-
UNIT matching is deferred (answer_unit=start.unit is wrong for cross-unit products
-> a unit gate would over-refuse; documented).
Honest practice measurement (sealed lane): 4 correct / 9 wrong / 37 refused
(baseline 3/0/47). +1 flip is the unambiguous whole-problem product (0021); the 9
wrongs are product-of-all eliminations on multi-step problems (caught by gold,
the learning signal). Whole-problem shapes add no coverage beyond the unambiguous
product WITHOUT cue precision: when product and sum both self-verify they disagree
-> uniqueness refuses (safe-but-low-coverage by design). The lever remains cue
precision (the ADR-0175 learning loop).
Microscope finding: 0003-class flips (48*24*0.75=864=gold) are blocked by a
DECIMAL/currency grounding gap -- '$0.75' tokenizes to 0/75 so '0.75' is not
grounded by the shared round-trip primitive. Not a search bug; deferred
extraction-richness work (won't casually change the serving round-trip primitive).
A test documents the current refusal so the fix is detectable.
wrong=0: serving untouched (sealed); ambiguity + no-licensed-cue refuse; routes
through the proven gate. 8 MS-3 tests; full derivation surface 77/77; ruff clean;
smoke 67.
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