The first MEASURED, above-noise retrieval win this thread. The 75-q
n=3 audit_mode bench couldn't resolve any single lever (every failure
class <=3-5 q, sub the 5pp floor — four hypotheses died there). Fix
the instrument, not just the lever:
- bench/mine_questions.py + bench/recall_at_k.py: mine questions from
corpus titles (ground-truth target known by construction), grade by
deterministic retrieval recall@k via `query --dry-run` — no LLM, no
verifier, no n=3 noise, scalable to the 22K-deep numeral pool. The
curated qa_questions.txt stays the separate verifier-honesty/trap
gate; mined fixtures measure the answerable long tail per class.
- _numeral_fold_variants in query.py: ordinal-word ("Alexander the
second") <-> multi-char Roman ("Alexander II"), additive+symmetric,
unioned into _title_query_tokens exactly like _hyphen_fold_variants
(#000007). Strict 2..40 Roman set → no English-word collision;
single-char Romans (I/V/X) intentionally out of scope (universal
len>1 token filter — stated before building, ~4 of 10 residual
misses).
Measured on the mined numeral fixture: recall@8 22/40 (55%) -> 30/40
(75%), +20pp; 20 hits now rank-1. Discipline applied end to end:
measured-first, mirrored precedent, full-suite regression run (2482
passed, 0 regressions — numeral-fold is hot-path in
_title_query_tokens), real-path test (FakeSource->ingest->query()->
real _Hit, not a hand-built object), measured-after on a noise-free
instrument. The ~6 multi-char residual misses are a different
downstream cause the instrument now exposes for future iteration.
