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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 Its Phase B-E prescription (regex DerivedRecognizers via recognizer_match.py) is partially superseded by ADR-0164. ADR-0136 and its S-family (S.1 / S.2 / S.3 / S.4) have the same disposition: regex sentence-template prescription superseded; empirical refusal taxonomies and closed-set vocabulary preserved as lexicon seed.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.
77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
# ADR-0136.S.1 — Rate/Event Statement Parsing
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**Status:** Accepted — *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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**Parent:** ADR-0136 (Statement Layer Corridor) — see [ADR-0136 §Amendment 2026-05-26](./ADR-0136-statement-layer-corridor.md)
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**Date:** 2026-05-23
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## Context
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The GSM8K refusal taxonomy (`evals/gsm8k_math/train_sample/v1/refusal_taxonomy.json`)
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reveals that 23/50 cases are blocked by context-filler sentences (correctly
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refused — no parseable numeric state), while 4/50 have rate/capacity/price as
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their primary barrier. The remaining cases are compound-statement,
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distributive-multiply, and diverse long-tail shapes.
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This ADR targets the 4 rate-class cases with two closed statement shapes.
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## Taxonomy Finding
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| Primary barrier | Cases | S.1 scope? |
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|------------------------|-------|------------|
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| `context_filler` | 23 | No — correctly refused |
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| rate/capacity/price | 4 | **Yes** |
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| `compound_statement` | 5 | No |
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| `distributive_multiply`| 1 (+5 secondary) | No |
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| diverse long-tail | 17 | No |
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## Closed Verb Sets
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**Capacity verbs:** shuck, pick, pack, make, produce, type, read, write,
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paint, run, score, answer, complete (+ third-person -s forms).
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**Earnings verbs:** make, earn, receive, get, charge (+ third-person -s forms).
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No regex wildcards for verbs — every admitted verb is explicitly listed in a
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frozen set. Sentences with verbs outside the closed set are refused (not
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wrong).
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## Short-Circuit Rationale
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Both rate shapes bypass the Cartesian-product candidate graph because the
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rate computation is a direct `rate × time` multiplication with unit conversion,
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not a graph of initial-possessions and operations. The short-circuit runs
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before `_filtered_statement_choices` so that rate-shaped sentences don't
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trigger the "no admissible candidate" refusal.
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Actor matching is required: capacity questions with pronouns (`he`/`she`)
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accept any actor; named-actor questions require case-insensitive match.
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Mismatched actors produce refusal, not wrong answers.
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## Honest GSM8K Claim
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- **Pre-S.1:** 0/50 admitted (all refused).
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- **Post-S.1:** 1/50 admitted — `gsm8k-0014` (Bob shucks oysters) with
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answer 240.0 (correct).
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- **admitted_wrong = 0** (safety rail preserved).
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The other 3 rate-class cases remain blocked by context-filler sentences in
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their opening statements; the rate parsing behind them is irrelevant until
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those sentences parse.
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## Deferred
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- Context-filler gated problems (23 cases — needs semantic classification
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of narrative scene-setter sentences).
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- Conditional branching (overtime rules, e.g. "if she works more than 8 hours").
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- Percentage/interest rates (10% simple interest).
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- Multi-statement earnings (duration asserted in a separate sentence from the
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rate — needs general duration-statement parser).
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## Evidence
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- Axis lane: `evals/math_capability_axes/S1_rate_events/v1/` — 20/20 pass,
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wrong=0.
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- B3 bounded-grammar lane: unchanged (wrong=0).
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- GSM8K candidate-graph probe: wrong=0, admitted=1/50.
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- Tests: `tests/test_adr_0136_S1_rate_events.py` — ≥15 tests including B3
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regression guard and GSM8K admitted_wrong=0 rail.
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