GPT-5 is a reasoning model and is not bit-deterministic even at the API
level (no temperature override allowed). Variance testing in iter 7
showed ~1 in 6 loop runs hit a nondeterministic regression: in one run
the magazine_mead exemplar lost its italic markers around "New Yorker"
even though the exact same input had produced a clean output 5 times
prior. The scoring linter caught it (rule_magazine_name_italicized) but
the formatter still emitted the bad output to the user.
This commit adds a runtime guardrail:
- New module src/cmos/runtime_validator.py — type-independent
structural sanity checks. Operates on a single candidate string
with no Exemplar context, because at runtime we don't know the
source type. Checks: ends with period, no Ibid., has at least one
italic span (almost every CMOS bibliography entry italicizes
something), balanced * and " markers. Deliberately weaker than the
scoring linter; it's a fast guardrail, not a full validator.
- format_bibliography_entry now retries up to DEFAULT_MAX_RETRIES (2)
times when the validator rejects a candidate. Independent re-calls
are usually enough because the failures are stochastic. If every
attempt fails, the LAST attempt is returned (no exception) — the
caller still gets something usable, and the failure surfaces
through the scoring linter or human review. The retry path costs
zero on the common case (1 call per entry); ~1-2% extra calls on
noisy drafts.
Empirical: 3 consecutive loop runs after this change are scalar 1.000
canary 1.000 (vs 5/6 clean in the variance test before). Sample is too
small to claim full suppression but the signal is positive.
Also adds a new exemplar journal_multiauthor_secondary_first_last.toml
captured from the user's HML draft (Hasanah et al., IJIDI 2024). It
exercises the case where a multi-author entry has the first author
inverted and the rest in First Last form — which the iter 7 HML run
got wrong on one entry. Variance testing showed the exemplar passes
6/6 in isolation, so the original HML failure was nondeterminism, not
a missing rule. Keeping the exemplar regardless: it adds canary
coverage of a real-world multi-author pattern, no-DOI / JSTOR-URL
variant, and the year-suffix author-date holdover stripping.
Tests: 85 → 95 (8 new for runtime_validator + 2 new for formatter
retry). All passing.
Initial phase-1 baseline of the karpathy/autoresearch-style loop.
The formatter module is the inner-loop artifact; parser and linter
are infra. The linter carries a LINTER_VERSION hash (v0.2.0) that
will force a re-baseline on any rule change.
Components:
- harness/diff.py: case-sensitive field-level substring diff
- harness/score.py: three-axis scoring (field, linter, canary exact)
- src/cmos/linter.py: 9 CMOS 18 structural rules, each Purdue/CMOS cited
- src/cmos/parser.py: locate ## Bibliography section, split entries
- src/cmos/formatter.py: prompt + OpenAI call with caller injection
- src/cmos/cli.py: cmos format path/to/draft.md
- scripts/run_loop.py: loop runner with --fake mode for no-API runs
- exemplars/: 3 canary seed exemplars (book, journal w/DOI, web),
sourced from chicagomanualofstyle.org quick guide
Tests: 48 passing. Fake-mode baseline scalar = 0.000 on the 3 seed
exemplars (identity caller fails the linter on every rule). This is
the floor the real GPT-5 formatter needs to improve from.
cmos dev