docs(post-rat1): four parallel dispatch briefs (B/C/D/E)
After RAT-1's architecture audit exposed five systematically underbuilt components, A (the 4 missing injectors) is being taken by this agent. The other four are parallel-safe and each gets its own dispatch brief: B (Opus) — contemplation produces ratifiable claims (medium) C (Sonnet) — comprehension reader audit + decision (small→medium) D (Sonnet) — core teaching coverage CLI (small) E (Codex) — lexical ratification auto-compile (tiny) Each brief carries: operator profile, branch name, base, dispatch line, reads required FIRST, outcome inventory, hard requirements, tests, truth test. Anti-regression invariants enumerated. All parallel-safe — no shared file conflicts. The dispatch DAG names ALL FIVE pieces with A in-flight as a peer.
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docs/handoff/POST-RAT1-PARALLEL-BRIEFS.md
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# Post-RAT-1 Parallel Briefs (B, C, D, E)
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**Date:** 2026-05-27
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**Author:** Shay
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**Context:** Following the architecture audit (RAT-1 / PR #406), five components are structurally underbuilt. **A (the 4 missing injectors) is in flight as its own PR by Opus**. The remaining four are parallel-safe and each can be dispatched to its own operator.
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Each brief below is self-contained, copy-paste-runnable.
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---
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## Brief B — Make math contemplation produce **ratifiable** claims
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**Operator profile:** Opus (load-bearing — the claim shape is what an operator ratifies; getting it wrong is operator-burden risk)
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**Branch:** `feat/contemplation-ratifiable-claims`
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**Base:** `origin/main`
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**Estimated effort:** medium
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### Why
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Currently `core eval math-contemplation` produces proposals whose `proposed_change_payload` is:
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```json
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{"evidence_count": 8, "group_key": {...}, "modal_sub_type": "composition"}
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```
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This is evidence aggregation, not a ratifiable claim. The operator has to **design the claim from scratch** (pick a `surface_pattern`, pick a `composition_category`, pick a `polarity`) before calling `apply_composition_claim()`. The "operator just reviews" framing is misleading.
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### Outcome
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Make `teaching/math_contemplation.py::decompose_audit` (and the dispatcher in `teaching/math_contemplation_proposal.py`) emit proposals where `proposed_change_payload` carries:
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```json
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{
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"surface_pattern": "bound(count) × bound(unit_cost)",
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"composition_category": "multiplicative_composition",
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"polarity": "affirms",
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"evidence_count": 8,
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"group_key": {...},
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"modal_sub_type": "composition"
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}
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```
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— directly ratifiable by `apply_composition_claim()` with no operator-side design step.
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For `proposed_change_kind == "composition_reclassification"`, dispatch by `missing_operator`:
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- `quantity_extraction` → `multiplicative_composition` + `bound(count) × bound(unit_cost)` (currency-per-unit shape)
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- `multi_quantity_composition` → `additive_composition` + `bound(qty_a) + bound(qty_b)` (default; operator may edit)
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For `frame_reclassification`, `matcher_extension`, `injector_sub_shape` — keep current payload (those are still upstream of ratifiable claims).
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### Reads required FIRST
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- `teaching/math_contemplation.py::decompose_audit`
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- `teaching/math_contemplation_proposal.py` (proposal schema)
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- `teaching/math_composition_proposal.py::SAFE_COMPOSITION_CATEGORIES`
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- `teaching/math_composition_ratification.py::apply_composition_claim` signature
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### Hard requirements
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- Backward-compatible JSONL: existing tests that read evidence_count + group_key + modal_sub_type must still pass
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- Only `composition_reclassification` proposals get the enriched payload in v1 (frame/matcher/injector deferred)
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- `polarity` is always `"affirms"` (the audit row signals a real refusal — the operator can override to `"falsifies"` if needed)
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- `surface_pattern` must be in the operator's expected vocabulary (mirror the three SAFE patterns)
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- An end-to-end test that runs `core eval math-contemplation` then immediately feeds the first composition proposal into `apply_composition_claim()` without any field synthesis
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### Tests
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- `tests/test_contemplation_ratifiable_payload.py`:
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- 5+ test cases: each refusal pair yields a payload whose fields satisfy `apply_composition_claim`'s preconditions
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- Round-trip: proposal payload → ratification → no exception
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- Schema regression: existing fields still present
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- `tests/test_adr_0172_w2_decomposer.py` — update existing assertions
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### Truth test
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After this PR + a fresh `core eval math-contemplation`, the operator workflow becomes:
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```
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core eval math-contemplation
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core teaching review <composition-proposal-id> --accept --review-date YYYY-MM-DD
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```
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**without manually constructing a `MathReaderRefusalEvidence` or picking a category.**
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---
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## Brief C — Comprehension reader audit + decision
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**Operator profile:** Sonnet (investigation + documentation; minor wiring if needed)
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**Branch:** `docs/comprehension-reader-audit`
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**Base:** `origin/main`
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**Estimated effort:** small (investigation) — could escalate to medium if "operationalize" is chosen
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### Why
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The comprehension reader (`generate/comprehension/lifecycle.py` — `begin_sentence`, `apply_word`, `end_sentence`, `finalize`, `ProblemReadingState`, `EntityRef`, Phase 1/2 of ADR-0164) is substantial code that **admits zero cases in the math eval**.
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Direct measurement:
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```
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core eval gsm8k_math --split public --use-reader → 150/150 wrong=0 (same as without)
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train_sample --use-reader → 3/47/0 (same as without)
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```
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The reader exists but contributes nothing observable. Two possible truths:
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1. **It's load-bearing for something we don't measure** (cognition lane? semantic recall? answer rendering?)
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2. **It's a parallel R&D track that needs honest naming** as not-yet-operational on math
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### Outcome (investigation phase)
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Produce `docs/handoff/COMPREHENSION-READER-AUDIT.md` answering:
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1. Where in the live code path does `_try_comprehension_reader` actually run? Trace every caller.
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2. When `comprehension_reader_questions=True`, what specifically does the reader admit on the cognition eval lane (not just math)?
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3. Is the all-or-nothing discipline (one refusing sentence kills the whole reader path) the bottleneck on math? Or is the reader itself refusing on simple shapes?
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4. Are there ADR-0164 Phase 1/2 promises that aren't being honored?
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5. List 3 options:
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- **Operationalize**: change all-or-nothing → per-sentence so reader can contribute partial admissions
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- **Relabel**: honest doc update naming reader as "cognition track, not math substrate" (if true)
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- **Retire**: if the reader path duplicates capability that the regex/recognizer paths already provide
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### Hard requirements
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- No code changes in the investigation phase — pure read + doc
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- Audit must distinguish reader-on-math vs reader-on-cognition usage
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- Recommendation must be falsifiable (provide a measurable test for each option)
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- If "operationalize" is chosen, ship as separate PR after operator approval
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### Tests
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- None in audit phase. Implementation phase (if approved): operationalize path requires its own test plan.
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### Truth test
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After this brief: the project has a deliberate answer to "what does the comprehension reader do today, and what should it do?" Right now nobody knows. That's the bug being closed.
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---
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## Brief D — `core teaching coverage` CLI
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**Operator profile:** Sonnet (tight-scope CLI; mechanical aggregation)
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**Branch:** `feat/teaching-coverage-cli`
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**Base:** `origin/main`
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**Estimated effort:** small
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### Why
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There's no automated way to answer "given the current ratified state, what % of train_sample admits / refuses / wrong-counts by ShapeCategory?" We only see deltas by running the eval manually and eyeballing report.json. Flying blind on operator dispatch decisions.
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### Outcome
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New CLI: `core teaching coverage [--lane gsm8k_math] [--split train_sample] [--use-reader] [--json]`
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Behavior:
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1. Run the lane's runner if its report.json is stale (or always, if `--run`)
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2. Read the per-case verdict + refusal reasons
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3. Bin by:
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- `correct / refused / wrong`
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- Within refused: by `(refusal_mode, ShapeCategory)` — using the same categorization the position paper §4 table uses
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4. Emit a clean histogram with deltas vs the **last committed** report.json
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Example output:
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```
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Lane: gsm8k_math/train_sample/v1 (use_reader=true)
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Counts: correct=3 refused=47 wrong=0 (Δ from prior: 0 / 0 / 0)
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Refusal taxonomy:
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21 recognizer_empty_injection(discrete_count_statement)
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10 no_admissible_candidate
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5 recognizer_empty_injection(multiplicative_aggregation)
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4 recognizer_empty_injection(currency_amount)
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3 recognizer_empty_injection(rate_with_currency)
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2 recognizer_empty_injection(temporal_aggregation)
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2 recognizer_empty_injection(descriptive_setup_no_quantity)
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Wrong=0: ✓
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Case 0050 hazard pin: refused ✓
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```
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### Reads required FIRST
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- `evals/gsm8k_math/train_sample/v1/runner.py`
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- `evals/gsm8k_math/train_sample/v1/report.json` schema
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- `core/cli.py` existing teaching subcommands
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- `evals/refusal_taxonomy/shape_categories.py`
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### Hard requirements
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- Read-only (no eval lane mutation)
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- Delta comparison against the most recent **committed** report.json (uses `git show HEAD:evals/.../report.json` — if absent, no delta)
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- `--json` for CI integration
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- `--lane` defaults to `gsm8k_math`; `--split` defaults to `train_sample`
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- Refusal taxonomy is regex-pulled from `report.json[per_case][].reason` — no hardcoded category list
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- Exit code 0 on success regardless of counts (it's a report, not a gate)
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### Tests
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- `tests/test_teaching_coverage_cli.py`:
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- Fixture report.json with known counts → expected histogram
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- Delta path: stage old + new report → expected delta
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- `--json` schema
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- Empty/malformed report.json → clear error
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### Truth test
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After dispatch: every operator can run `core teaching coverage` after any ratification to see exactly which refusal modes their work moved (or didn't).
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---
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## Brief E — Lexical ratification auto-compile
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**Operator profile:** Codex (tiny mechanical; mirror RAT-1's pattern)
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**Branch:** `feat/lexical-ratification-auto-compile`
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**Base:** `origin/main`
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**Estimated effort:** tiny
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### Why
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RAT-1 (PR #406) added `compile_pack()` auto-call at the end of `apply_frame_claim` + `apply_composition_claim` so source-file writes immediately reach the runtime. **`apply_lexical_claim` was deliberately skipped** because the existing `language_packs/compiler.py` already compiles `lexicon.jsonl`. But the lexicon compiler runs at pack-build time, not after a runtime ratification.
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So today: `core teaching` ratifies a LexicalClaim → writes `lexicon/{category}.jsonl` → the **next runtime turn doesn't see it** because nothing triggers re-compile + manifest update.
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### Outcome
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Extend `teaching/math_lexical_ratification.py::apply_lexical_claim` to call `compile_pack()` at the end of a successful ratification — same pattern RAT-1 used for frame + composition.
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Plus: ensure `compile_pack()` regenerates the lexicon compiled artifact `lexicon.jsonl` AND updates `manifest.checksum`. Currently RAT-1's `compile_pack` only handles frames + compositions; this brief extends it.
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### Reads required FIRST
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- `teaching/math_lexical_ratification.py::apply_lexical_claim`
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- `language_packs/compile_pack.py` (the RAT-1 helper)
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- `language_packs/compiler.py::_load_pack_cached` (existing lexicon compile)
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- `generate/comprehension/lexicon.py::load_lexicon` (the runtime consumer)
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### Hard requirements
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- `wrong == 0` preserved (no test moves wrong)
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- The existing lexicon checksum SCHEME stays the same — just regenerated more frequently
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- Mirror RAT-1's `tests/test_math_{frame,composition}_ratification.py` update — `test_lexicon_checksum_preserved_by_lexical_ratification` (manifest may change; lexicon checksum re-derives from compiled bytes)
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- Idempotent: running ratification twice doesn't bump checksum unless source bytes changed
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- Existing `core teaching compile-pack` command should pick up lexical changes too — extend the receipt to include `lexicon_checksum` + `lexicon_bytes_written`
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### Tests
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- `tests/test_lexical_ratification_auto_compile.py`:
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- Ratify a LexicalClaim → compile fires → lexicon registry reload sees the new entry
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- Idempotent: second ratify with same evidence → no compile mutation
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- Lexicon-checksum-preserved-across-ratify (with new bytes)
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### Truth test
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After this PR: a LexicalClaim ratification reaches the runtime within one turn, matching the frame + composition discipline RAT-1 established.
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---
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## Dispatch DAG
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```
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RAT-1 (PR #406) — base for all four briefs
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│
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├──── A (Opus, in-flight) — 4 missing injectors
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│
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├──── B (Opus) — contemplation ratifiable claims
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│
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├──── C (Sonnet) — comprehension reader audit
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│
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├──── D (Sonnet) — coverage CLI
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│
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└──── E (Codex) — lexical auto-compile (tiny)
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```
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All four briefs are parallel-safe — no shared file conflicts. Each touches different modules.
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## Anti-regression invariants (all four)
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- `wrong == 0` on `core eval gsm8k_math --split public` preserved (150/150)
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- Case 0050 hazard pin holds
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- `engine_state/*` never committed
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- ADR-0166 — no new eval lanes
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## Memory pointers
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- [[milestone-me1-me5-matcher-extensions-complete]] — the wave that exposed the gaps
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- [[project-ratification-consumption-gap-2026-05-27]] — the original finding
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- [[feedback-ratify-vs-consume-loop-closure]] — the general pattern
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---
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## Copy-paste dispatch (per brief)
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```text
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# Brief B
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Read docs/handoff/POST-RAT1-PARALLEL-BRIEFS.md §"Brief B".
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git fetch origin main && git worktree add /tmp/wt-brief-b origin/main && cd /tmp/wt-brief-b && git checkout -b feat/contemplation-ratifiable-claims
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# Brief C
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Read docs/handoff/POST-RAT1-PARALLEL-BRIEFS.md §"Brief C".
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git fetch origin main && git worktree add /tmp/wt-brief-c origin/main && cd /tmp/wt-brief-c && git checkout -b docs/comprehension-reader-audit
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# Brief D
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Read docs/handoff/POST-RAT1-PARALLEL-BRIEFS.md §"Brief D".
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git fetch origin main && git worktree add /tmp/wt-brief-d origin/main && cd /tmp/wt-brief-d && git checkout -b feat/teaching-coverage-cli
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# Brief E
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Read docs/handoff/POST-RAT1-PARALLEL-BRIEFS.md §"Brief E".
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git fetch origin main && git worktree add /tmp/wt-brief-e origin/main && cd /tmp/wt-brief-e && git checkout -b feat/lexical-ratification-auto-compile
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```
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