e705f27d2e
Replace the regex sentence-template front-end of the math admissibility layer with an incremental compositional reader. Lock the architectural boundary that regex is permitted only at the lexeme level, never as sentence-structure templates. ADR-0164 (Proposed) — Incremental Comprehension Reader. Word-by-word state accumulation over a closed set of semantic categories, with the operational lexicon living as a pack-shaped data artifact under language_packs/data/en_core_math_v1/. Reader output type matches the existing regex parser's output, so the binding-graph admissibility (ADR-0132/0133/0134/0135), the solver (ADR-0116), and the verifier (ADR-0117) stay unchanged. wrong=0 is preserved by construction — the reader produces inputs to the existing admissibility gate, not a bypass around it. Phased coexistence with the regex layer during transition; regex sentence templates removed in Phase 3. ADR-0165 (Proposed) — Regex Scope Rule. Structural invariant: regex matches one piece of orthographic material with a closed rule (currency literal, fraction literal, percentage, time-amount, closed unit-noun sets), never a sentence shape. Lexeme-primitive registry is closed and grown through the same contemplation -> proposal -> HITL review corridor that grows vocabulary (ADR-0150 / 0152 / 0155 / 0161). The engine acquires new recognition tools through reviewed teaching, not through operator edits to parser code. ADR-0163's diagnosis (front-end is the bottleneck) is reaffirmed. Its Phase B-E prescription (regex DerivedRecognizers via recognizer_match.py) is partially superseded by ADR-0164. ADR-0136 and its S-family (S.1 / S.2 / S.3 / S.4) have the same disposition: regex sentence-template prescription superseded; empirical refusal taxonomies and closed-set vocabulary preserved as lexicon seed. The HITL corridor architecture is preserved; what flows through it changes from regex recognizers to lexicon entries, categories, and lexeme primitives. Session log SESSION-2026-05-26-comprehension-reader.md captures the narrative of how this decision emerged from the post-D.2 train-sample baseline review (correct=3 refused=47 wrong=0, 34/47 refusals at the question gate). No runtime code changes. ADRs only.
106 lines
4.3 KiB
Markdown
106 lines
4.3 KiB
Markdown
# ADR-0136.S.2 — Conditional-Op Question (Statement-Layer Corridor)
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**Status:** Active — *regex patterns scheduled for removal under [ADR-0164](./ADR-0164-incremental-comprehension-reader.md) Phase 3; closed-set vocabulary preserved as lexicon seed*
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**Date:** 2026-05-23
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**Parent:** [ADR-0136](./ADR-0136-statement-layer-corridor.md) — see [ADR-0136 §Amendment 2026-05-26](./ADR-0136-statement-layer-corridor.md)
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---
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## Context
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After S.0 (context-sentence classifier) and S.1 (rate/event statement parsing)
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landed, the GSM8K train-sample probe sat at **2/50** admitted (`gsm8k-0014`,
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`gsm8k-0018`), `wrong == 0`.
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A taxonomy pass over the remaining 48 refused cases identified `gsm8k-0042`
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as the next single-barrier unlock:
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```
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Ella has 4 bags with 20 apples in each bag and six bags with 25 apples in
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each bag. If Ella sells 200 apples, how many apples does Ella has left?
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```
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The initial-state sentence already parses via `_CONJ_EMBEDDED_RE` (ADR-0131.G.4
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embedded quantifier) to `InitialPossession(entity="Ella", value=230,
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unit="apples")`. The **only** barrier is the question form, which neither
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`_Q_ENTITY_RE` nor `_Q_TOTAL_RE` cover: the `If <Entity> <verb> <N> <unit>,
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how many <unit2> does <Entity2> <aux> [left|...]?` shape combines a
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conditional-action operand with the entity-recall question.
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---
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## Decision
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Add a **conditional-op question** extractor and a corresponding short-circuit
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in `parse_and_solve` that:
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1. Matches the closed shape `If <Entity> <verb> <N> <unit>, how many <unit2>
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does <Entity2> <aux> [<qualifier>]?`
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2. Classifies `<verb>` against two closed sets:
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- `_COND_SUBTRACT_VERBS` (sell/sells/sold, give/gives/gave, eat/eats/ate,
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use, lose, spend, donate, remove, take, send, pay, drop, throw)
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- `_COND_ADD_VERBS` (buy/buys/bought, get/gets/got, receive, find, add,
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collect, pick, earn, gain)
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3. Refuses on any of: unknown verb, unit mismatch (`<unit>` vs `<unit2>`
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after canonicalization), entity mismatch (`<Entity>` vs `<Entity2>`
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case-insensitively), `N <= 0`.
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4. In `parse_and_solve`: if the question yields exactly one
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`CandidateConditionalOpQuestion`, collect all `extract_initial_candidates`
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from every statement sentence and look for **exactly one** matching IC
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by `(entity, unit)`. If found, compute `initial_value ± operand` by verb
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polarity; emit only when `answer >= 0`. Refuses otherwise.
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The short-circuit is structurally identical to the S.1 capacity/earnings
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paths: it bypasses graph construction, returns `selected_graph=None`, and
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preserves `wrong == 0` by refusing rather than guessing on any ambiguity.
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---
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## Invariants
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- **`admitted_wrong == 0`** preserved by:
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- Single-match (entity, unit) requirement before emission
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- Non-negative answer gate
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- Closed verb sets — no wildcards
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- **Context-filler safety rail** unchanged (S.0)
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- **No solver/graph/verifier changes** — extractor lives in
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`math_candidate_parser.py`; short-circuit lives in `math_candidate_graph.py`
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---
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## Honest GSM8K delta
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| Stage | Admitted | Wrong |
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|---|---|---|
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| Pre-S.0 | 0/50 | 0 |
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| Post-S.1 | 1/50 (`0014`) | 0 |
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| Post-S.0 classifier | 2/50 (`+0018`) | 0 |
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| **Post-S.2** | **3/50** (`+0042`) | **0** |
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`gsm8k-0042` admits with `answer == 30.0` (expected: 30).
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---
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## Consequences
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- The S.x corridor now spans `parser` (S.1 capacity/earnings, S.2 question
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shape) and `pre-pass classifier` (S.0). Future phases (S.3 compound
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statements, S.4 coreference) extend this pattern.
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- The canonical GSM8K runner (`evals/gsm8k_math/runner.py`) still asserts
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`selected_graph is not None` on admission and therefore cannot score the
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short-circuit admissions. The `report.json` artifact remains stale
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(0/50/0); the honest count is asserted via direct `parse_and_solve` test
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(`test_gsm8k_post_s2_admission_honest`). Aligning the canonical runner
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with the short-circuit paths is deferred (out of S.2 scope).
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---
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## Deferred
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- Conditional-op question forms with **more than one** operand (e.g.
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"If she gives away 3 and buys 5, …") — needs two-op composition.
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- Question forms without `does <Entity>` aux (e.g. "how many apples
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remain?") — needs a sibling regex.
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- Conditional questions where the conditional and the question reference
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**different** units that are unit-related (e.g. dollars↔cents) — needs
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unit-relation taxonomy.
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