core/docs/analysis/comprehension-coverage-wall-map-2026-06-14.md

Wall map: the recognizer/coverage wall is COMPOSITION

Date: 2026-06-14 Status: diagnostic map (the real capability lever, per the C decision — point at the recognizer/coverage wall). Evidence: committed refusal taxonomy + live lane runs

• 3× prior empirical confirmation (ADR-0191/0192/0193 milestones). Bottom line: more single-shape recognizers are proven metric-inert. The wall is that shape-recognizers do not compose within a compound statement or across statements with coreference. The capability gain lives in compositional reading, not in widening any one shape.

Evidence

1. GSM8K diagnostic — the composition signature

train_sample serving: 4 admitted / 0 wrong / 46 refused (8% admission). The curated refusal taxonomy (refusal_taxonomy_v3.json, 50 cases) shows no single barrier exceeds 5/50 (10%) and the barriers are fragmented across ~24 primary categories:

primary_barrier (top):  compound_statement 5 · novel_initial_form 5 ·
  novel_initial_verb 4 · fraction_operand 4 · conditional_question 3 ·
  context_filler 3 · compound_comparative 3 · rate_price 2 · … (long 1-2 tail)
secondary_barriers (co-occurring): compound_comparative 5 · percentage_of 5 ·
  fraction_operand 4 · rate_price 4 · multi_step_complex 4 · rate_comparative 4 ·
  coreference_pronoun 3 · …

The secondaries are the tell: refused cases stack 3-4 structures at once (a compound statement with a comparative and a fraction operand and a pronoun coreference). The two raw refusal modes confirm it:

• recognizer matched but produced no injection (majority) — the category is recognized (discrete_count_statement, rate_with_currency, multiplicative_aggregation, …) but the injection layer can't structure the composed sentence.
• no admissible candidate — the composed shape isn't recognized at all.

2. Capability lanes are narrow curated-gold conformance, not open coverage

• evals/combined_rate_oracle: 19/19 valid, by_expect = {solved: 6, solver_refuses: 5, reader_refuses: 8} — the combined-rate reader handles exactly 6 solvable shapes; everything else it correctly refuses. It is an oracle (does the reader match the curated gold), not a coverage measure.
• evals/comprehension/*: per-domain conformance runners (propositional, syllogism, set-membership, total-ordering, relational metric/predicate) — the flagship deductive lanes, each 100%-conformant to a curated gold.

So each capability is a shape-specific reader at 100% of a small gold. Capability = gold scope. The readers are individually correct and individually narrow.

3. Single-shape widening is metric-inert (already proven 3×)

ADR-0191/0192/0193 confirmed empirically that adding one operator/recognizer is metric-inert on the real corpus — because the refused cases need composition, not one more shape. This map's barrier data is the fourth confirmation.

Diagnosis

The wall is compositional reading: the organ recognizes individual structures (rate, fraction, comparative, count, percentage, coreference) but cannot compose them — combine multiple recognized sub-structures within one compound clause, and carry referents across statements. Real GSM8K problems are compositions; the readers are a bank of isolated shape-recognizers. That is why admission caps at ~8% and why every single-shape fix has been inert.

Leverage (two tiers, honestly)

• Tier 1 — near-term recognizer coverage (small, capped): the pure single-barrier misses (novel_initial_form/novel_initial_verb and a few unrecognized rates/temporals like "Every week, he gets 6 cards", "Mark does a gig every other day for 2 weeks") can be admitted by adding their recognizers. Honest estimate: ~+5 cases (4→~9/46), then it hits the composition wall hard, because the remaining ~37 refused cases carry multiple co-occurring barriers that one recognizer doesn't clear.
• Tier 2 — compositional reading (the real lever): a layer that composes recognized sub-structures within a compound statement and resolves coreference across statements, so a sentence that is rate+comparative+fraction+pronoun reads as one composed structure. This is the only path past ~10/46, and it is an architectural build (not a recognizer addition). It must preserve wrong=0 (a composed reading still passes the self-verification/disagreement gate).

Recommendation

Do not pour effort into more isolated shape-recognizers — it is proven inert. The capability gain is Tier 2 (compositional reading). Tier 1 is a small honest warm-up at best; I would not lead with it.

Tier 2 is make-or-break and the solution space is wide (how to compose, where the composition layer sits relative to extract/clauses/compose, how coreference is resolved deterministically, how composed readings stay wrong=0). That warrants a design effort — multiple independent architecture attempts, judged and synthesized — before any build. Proposed as the next step, on sign-off.