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>
72 lines
4.2 KiB
Markdown
72 lines
4.2 KiB
Markdown
# speaker-attribution
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Limits found by cross-checking the 2026-08-11 chapter run against the panel images.
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## Nothing attributes a speaker in a multi-character panel {#tail-is-not-geometry}
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The false `tail` label is gone (`decisions/speaker-attribution.md#no-fake-tail`). What replaced it is a
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refusal, not an answer: with two or more characters present, every speech line now returns `unknown`.
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On a chapter like this one that costs 31 of 81 speech lines their speaker. The narration then falls back
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to a `generic-handle`. That is the honest floor, and it is not the fix.
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The measurement that forced it, on job `778297bc`, chapter `7c944dd4`. Three two-character panels were
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checked against the art. All three are wrong, each with the two speakers swapped:
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| panel key | line | truth | pipeline |
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| --- | --- | --- | --- |
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| `p010.png` | "…definitely an Egen guy, Seonho!" | the woman | Seonho, the person addressed |
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| `p010.png` | "Y-you think so?" | Seonho | Choi Haeseon |
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| `p012.png` | "Want me to send you the link?" | the woman | the man |
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| `p059.png` | "If team leader Choi says it, it must be true." | the man | Choi Haeseon |
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The last row needs no image: the line refers to Choi in the third person and is attributed to Choi.
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The grounded path exists and almost never fires. Only 2 of 81 speech lines got `som_face`, because
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attribution marks need `face_detect` boxes that survive `_pair_faces_to_present`, and these webtoon
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close-ups rarely produce them. Inference, not measured: the face detector was not instrumented.
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`worker_vision.py:356` already carries the `ponytail:` note that multi-character attribution needs
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per-balloon geometry. `bubble_detect.py:9` records that the `det`/`seg` heads carry balloon fill and
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tail tips and are unused.
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**Revisit trigger:** the share of narrated lines with a named speaker is the Phase 1 headline metric
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(`ROADMAP.md`). The 30% read on this run counted attributions the sample says are wrong. The next run
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will read lower and will be the first honest number. Raising it means binding a balloon to a speaker
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by tail geometry, using the unused `det`/`seg` heads.
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## One character id covers two different women {#identity-over-merge}
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`character_afa7623b` is stored as "black bob, white sweater" and is assigned both to that person
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(`p059.png`) and to the brown-bob green-top coworker (`p010.png`, `p012.png`). It took 25 of the 26
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identity assignments in the chapter, against 113 detected people. Coverage is 23%.
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**Revisit trigger:** any work on the identity Tier-2 decider. A single id absorbing a whole chapter is
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the signature to watch for.
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## The character registry carries five weeks of wrong names {#registry-pollution}
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The registry holds 53 characters for manga `ef105a86`, 41 of them unnamed, with "Kei" three times and
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"Kanade" twice. Kei, Kanade, Zen, Rico, K3, and Watanabe occur zero times in this chapter's text. Only
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Haeseon and Seonho do. `/stage/clear` leaves the per-manga registry intact by design, so every rerun
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inherits the whole pile.
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Duplicate rows also make a correct name unresolvable. This manga holds `Choi Haeseon`, `Seonho` with
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aliases `["Lim Seonho", "Seonho"]`, and a separate `Lim Seonho`. An answer of "Lim Seonho" matches two
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rows, so `normalize_speaker` returns candidates and raises `ambiguous-speaker` instead of binding. The
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pipeline read the name correctly and still cannot name the speaker.
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**Revisit trigger:** before any run that is meant to produce a clean baseline. Either scope the
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registry to a chapter or add a reviewed reset. Merging the duplicate rows needs the reversible-merge
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design first (`caveats/audit-open.md#destructive-reconcile`).
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## One invented word still halts the chapter {#one-word-halts-chapter}
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The multi-word name failure is fixed (`decisions/speaker-attribution.md#multiword-cast-names`). The
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blast radius it exposed is not. `run_stage_script` retries a rejected beat once, then raises, so a
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single unsupported token ends the run at that beat. On job `778297bc` one of the 29 lost beats cited
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`['Blur']`, an onomatopoeia the model invented. The verifier was right, and the whole chapter still
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stopped.
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**Revisit trigger:** the next `unsupported-proper-noun` halt that is a true positive. The likely answer
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is to flag the beat for review and continue, which is `#136` gate work, not a verifier change.
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