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.
Add 11 new exemplars pulled verbatim from the CMOS official quick guide
(chicagomanualofstyle.org/tools_citationguide/citation-guide-1.html):
- book_two_authors_binder: Binder & Kidder (two-author book)
- book_chapter_doyle: Doyle in Marks & Parkin (chapter in edited volume,
no page range per CMOS 18)
- book_translated_liu: Liu Xinwu, trans. Tiang (translated book, non-
Western name not comma-inverted)
- book_edition_borel: Borel, 2nd ed. via EBSCOhost (edition + database)
- book_ebook_roy: Roy, Kindle format
- journal_many_authors_snyder: 7 authors in PLOS ONE, exercises the
CMOS 18 "first 3 + et al." threshold and article-ID page format
- magazine_mead: New Yorker (tests leading-"The" drop)
- newspaper_blum: NYT with URL (tests abbreviation expansion)
- social_media_cmos_facebook: Facebook post with case preservation
- podcast_ober: Pushkin podcast with season/episode/duration
- video_cowan_ted: TED Talk with venue and duration
Every exemplar is marked canary=true — we want byte-exact regression
detection on all of them. Source citations and CMOS rule justifications
are in the TOML doc comments.
All 14 canonical outputs lint clean under LINTER_VERSION v0.2.0. The
harness will score them in the following commit.
First real loop iteration found that GPT-5 silently drops date qualifiers
("Effective", "Published", "Accessed", etc.) when reformatting web
sources. The field diff was satisfied because the canonical date field
did not include the qualifier, but the canary exact-match axis caught
the regression.
Two fixes, per the canary-upstream policy:
1. Tighten the web-page exemplar canonical: date field now includes the
"Effective" qualifier so the field diff will catch future regressions
without relying on the canary.
2. Add SYSTEM_PROMPT rule 12 instructing the formatter to preserve
semantic date qualifiers from the input.
After these fixes: scalar 1.000, canary exact-match 1.000 on all three
seed exemplars.
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