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