kami
a965077e6b
_set_of_mark labels a detected face `unknown` when gated pairing matched it to no present character. An answer pointing at that mark grounds nothing, yet it carried som_face, the highest-trust provenance. All 7 som_face lines in the first 36 panels of the rerun were this case. Same defect class as the fake tail label. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
70 lines
4.0 KiB
Markdown
70 lines
4.0 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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## The model's speaker answer is resolved against what the prompt showed {#prompt-label-answers}
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**Closed.** `build_dialogue_prompt` renders a present character as
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`- person_1: brown ponytail, green dress (f)`. gemma answers with any part of that line, so every part
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of it must map back to the `local_id`. It did not, and `normalize_speaker` classified each unmatched
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answer as a free-form name that no registry entry could match.
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Evidence, measured on 36 panels of the cancelled first rerun. 15 lines carried a description and 9 a bare
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`local_id` with no identity assignment. 2 carried a stale `P1` mark label, 2 a name with the gender
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marker attached. That is 28 of 51 speech lines. Only 3 resolved to a `character_id`.
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`_apply_speaker_labels` now takes `present` and resolves the id, the name, the description, and each of
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those plus the gender marker. A key shared by two present characters is dropped, because it identifies
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neither. An id-shaped answer naming nobody present becomes `unknown`. A trailing gender marker is
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stripped, so an off-panel `Seonho (m)` still matches the registry name `Seonho`.
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`normalize_speaker` refuses an id-shaped value independently, because the worker is a separate process
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and the contract is load-bearing (invariant 7).
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Forbids: showing the model a label the worker cannot resolve back, and treating an unmatched speaker
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string as a name.
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Check: `python worker_vision.py`, the `shown`/`twins` cases. `pytest test_correctness.py`,
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`test_an_id_shaped_speaker_is_never_a_name`.
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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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