kami
e8941d8ceb
_annotate_speaker_methods stamped `tail`, the highest-trust provenance, on any line whose speaker matched a present local_id, at gemma's confidence of 1.0. No balloon was read. Three of three sampled two-character panels had the speakers swapped, so a multi-character guess is now dropped to unknown, and a solo-panel guess is kept as model_solo at 0.7. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
46 lines
2.6 KiB
Markdown
46 lines
2.6 KiB
Markdown
# Speaker attribution and cast names
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Settled 2026-08-11 from the quality cross-check of job `778297bc`
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(`JOURNAL.md`, `caveats/speaker-attribution.md`). No GPU work ran and no pipeline run was executed
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after the change. Both claims rest on source and on the CPU-only self-checks named below.
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Files: `worker_vision.py` on workpc, `correctness.py` and `test_script_verify.py` in the homesrv
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orchestrator (`/mnt/server/home/kami/docker-apps/manga-infra/orchestrator/`).
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## A model guess is never labelled `tail` {#no-fake-tail}
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**Closed.** `speaker_method` names how a speaker was established, and nothing may claim geometry it did
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not read. `_annotate_speaker_methods` stamped `tail`, the highest-trust label, on any line whose
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speaker matched a `local_id` present in the panel, keeping gemma's confidence of 1.0. No balloon was
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ever consulted.
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Evidence: three of three sampled two-character panels had both speakers swapped
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(`caveats/speaker-attribution.md#tail-is-not-geometry`). 31 of 81 speech lines carried `tail` with two
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or more characters present.
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The label is gone. With two or more characters present the guess is dropped: `speaker` becomes
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`unknown`, confidence 0.0, method `unknown`. With one character present the claim equals the solo
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backstop, so it is kept as `model_solo` at confidence 0.7. Grounded `som_face` and `solo_prior` rows are
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untouched, because the function still skips any row that already carries a method.
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Forbids: minting a provenance label for evidence that was not read, and shipping a multi-character
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attribution as truth before balloon geometry exists.
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Check: `python worker_vision.py`, the `crowd`/`lone` cases.
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Cost: the named-speaker share will fall. The 30% headline was measured on attributions the sample says
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are wrong, so the lower number is the first honest one.
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## Cast names enter the verifier tokenized {#multiword-cast-names}
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**Closed.** `verify_script` compares single capitalized tokens, so every allowed name must be present as
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tokens. `allowed` was built from `cast_names` verbatim, which put `"choi haeseon"` in the set as one
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string while the checker looked up `Choi` and `Haeseon` separately.
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Evidence: the `script` stage failed at 87/116 on job `778297bc`. 28 of the 29 lost beats cite
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`unsupported-proper-noun: ['Choi', 'Haeseon']`. A one-word name such as `Seonho` always passed, which
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is why this survived the Phase 1 verifier work
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(`decisions/audit-phase1.md#verifier-false-positives`).
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Forbids: adding any future allow-list to `verify_script` as whole strings.
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Check: `pytest test_script_verify.py`, `test_multiword_cast_name_is_supported`.
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