Brief D from PR #407. Closes the "flying blind on per-shape coverage"
gap identified in RAT-1's audit (finding 6).
After this PR, every operator can run a single command to see exactly
which refusal modes their work moved (or didn't), without re-eyeballing
report.json by hand.
Modules
-------
- teaching/coverage.py — pure aggregator:
- _classify_refusal — maps each per-case refusal reason to a
stable bucket (recognizer_empty_injection(<ShapeCategory>),
no_admissible_question, no_admissible_statement,
unexpected_question_count, other)
- build_coverage_report — reads a lane's report.json + emits a
CoverageReport with counts, refusal_taxonomy (sorted by count
desc), case_0050_verdict, optional delta vs baseline
- fetch_committed_baseline — uses `git show HEAD:<relpath>` to
pull the baseline report.json for delta computation
- core/cli.py:
- cmd_teaching_coverage — formats the report for terminal output
- core teaching coverage [--lane gsm8k_math] [--split train_sample]
[--version v1] [--use-reader] [--run] [--delta] [--json]
CLI output example
------------------
Lane: gsm8k_math/train_sample/v1 (use_reader=True)
Counts: correct=3 refused=47 wrong=0
Refusal taxonomy:
21 recognizer_empty_injection(discrete_count_statement)
6 no_admissible_statement
5 recognizer_empty_injection(multiplicative_aggregation)
4 no_admissible_question
4 recognizer_empty_injection(currency_amount)
3 recognizer_empty_injection(rate_with_currency)
2 recognizer_empty_injection(descriptive_setup_no_quantity)
2 recognizer_empty_injection(temporal_aggregation)
Wrong=0: ✓
Case 0050 hazard pin: refused ✓
Tests (13 new)
--------------
tests/test_teaching_coverage_cli.py — classification narrowness,
counts aggregation, case 0050 verdict capture, delta computation,
missing-baseline path, missing-report error, taxonomy sort order,
wrong=0 invariant visibility via as_dict.
Suite results
-------------
core test --suite teaching -q → 106 passed (93 → +13)
core test --suite runtime -q → 20 passed
core test --suite packs -q → 127 passed
core eval gsm8k_math --split public → 150/150, wrong=0
Note on Brief E (lexical auto-compile): the audit was WRONG. The
lexicon loader (generate/comprehension/lexicon.py::load_lexicon)
reads from the per-category source files directly; the compiled
lexicon.jsonl is only a manifest-checksum pin, not the source of
truth at runtime. apply_lexical_claim() writes a new entry → next
turn the loader sees it. Brief E is a non-issue; closing without a
code PR.
Verified by direct test: stage a clone of the math pack, write a
synthetic lemma to drain_token.jsonl, clear the lexicon cache, load
again → new entry present. So 3 of the 5 audit gaps closed (A, D,
E-as-correction); B and C remain as the next operator dispatch
targets.
Independent of PR #406 (RAT-1) and PR #408 (WAVE-A). Based on main.
Shay