Record all six defects and the 9% baseline
Adds decision entries for the unpaired set-of-mark label, the interjection verifier false positive, and the vision-blob clearing bug, plus the per-run speaker audit script used to measure the chapter. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
+64
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@@ -1,71 +1,86 @@
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# HANDOFF, 2026-08-11 session
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# HANDOFF, 2026-08-11 late session
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Live state lives in `NEXT.md`. This file is only what this session did.
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Live state is in `NEXT.md`. This file is only what this session did.
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## Asked
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1. Add the buckets to rustfs.
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2. Fix the bucket saving on the orchestrator side.
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3. Run the title recap.
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Continue from the previous handoff: fix speaker attribution, then rerun and re-read the named-speaker
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share. Mid-session: what happens to unidentified people in a two-person panel, can a known character be
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mis-named, and keep watch during the run.
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Mid-session: check `manga.kvmx.ru`, and cross-check whether the run got dialogue and identities right.
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## Result
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The named-speaker share is **9 of 95 speech lines, 9%**. The previous 30% counted fake tails. All 9 binds
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are `Choi Haeseon`, the over-merged row. `script` passed 116/116 for the first time.
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**Identity, not attribution, is now the constraint.** 26 of 113 detected people carry an identity. 25 of
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the 26 are that one row. This chapter caps near 23% named even with perfect balloon binding.
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Six defects, four found by measuring the run rather than by reading code. Each has a decision entry:
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| # | defect | entry |
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| --- | --- | --- |
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| 1 | `tail` stamped on a model guess at confidence 1.0 | `decisions/speaker-attribution.md#no-fake-tail` |
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| 2 | a two-word cast name always failed the script verifier | `#multiword-cast-names` |
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| 3 | `/stage/clear dialogue` deleted nothing and reported success | `decisions/storage-layout.md#clear-vision-blob` |
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| 4 | gemma's speaker answer echoed the prompt label, became a name | `#prompt-label-answers` |
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| 5 | `som_face` stamped on a mark that paired to nobody | `#unpaired-mark` |
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| 6 | one two-letter interjection halted the chapter at 112/116 | `#interjection-false-positive` |
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## Changed
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Workers, `/home/kami/Programs/n8n-worker`, branch `restore-runtime`:
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Workers, `/home/kami/Programs/n8n-worker`, branch `restore-runtime`, commits `e8941d8 8071137 a965077`
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plus docs:
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- `worker_crop.py` panels -> `s3://panels/`
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- `worker_tts.py` wavs -> `s3://audio/`
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- `worker_layers.py` layers -> `s3://layers/`
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- `worker_render.py` clips and `chapter.mp4` -> `s3://video/`
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- `decisions/storage-layout.md`, `caveats/speaker-attribution.md`, indexes, `JOURNAL.md`, `NEXT.md`
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- `worker_vision.py`: `_annotate_speaker_methods`, `_apply_speaker_labels`, new `_present_keys`
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- `decisions/speaker-attribution.md` (new, 5 sections), `decisions/storage-layout.md#clear-vision-blob`,
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`caveats/speaker-attribution.md`, `caveats/audit-open.md#dishonest-clearing`, both indexes,
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`JOURNAL.md`, `NEXT.md`
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Orchestrator, `/mnt/server/home/kami/docker-apps/manga-infra/orchestrator`:
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Orchestrator, `/mnt/server/home/kami/docker-apps`, commits `b18b6b4 603d388 db8d7c5 ccc3a8e` plus the
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interjection commit:
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- `minio_layout.py` per-artifact bucket constants, `BUCKETS`, `parse_key` accepts any of them
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- `service.py` `_s3_delete_prefix` takes `<bucket>/<prefix>`, `_stage_s3_prefixes` no longer slices,
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new `_ensure_buckets` in the lifespan
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- `test_minio_layout.py` updated, one test added
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Infrastructure, homesrv:
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- six buckets created on `rfs` (rustfs, `127.0.0.1:9010`)
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- `manga-fetch` started, exited 2 weeks, `/job/create` fails without it
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- `manga-web` started, exited 2 weeks, `manga.kvmx.ru` -> nginx -> `localhost:8083`
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- orchestrator rebuilt and restarted
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- `mc` aliases `mio` and `rfs` now exist on homesrv
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- `correctness.py`: tokenized `allowed`, `_ID_SHAPED` guard in `normalize_speaker`, interjection
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stopwords, short-quote grounding skip
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- `db.py`: `_STAGE_VISION_KEYS` and the strip pass in `clear_stage_data`
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- `test_script_verify.py`, `test_correctness.py`, `test_db.py`: one case each
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## Measured
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Job `778297bc-e7ce-439d-91b5-8a027060d17f`, chapter `7c944dd4-e972-42c7-ba60-9f6939548e80`,
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"Teto X Egen", 116 panels.
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Job `778297bc-e7ce-439d-91b5-8a027060d17f`, chapter `7c944dd4-e972-42c7-ba60-9f6939548e80`, 116 panels.
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- 109 orchestrator tests pass. `worker_crop.py`, `worker_tts.py`, `worker_render.py` self-checks pass.
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`worker_layers.py` self-check fails on the missing `legacy/qwen_layered_workflow.json`, which
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predates this session.
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- buckets after the run: `raw` 64MiB/79, `panels` 101MiB/116, `manga` 366MiB/491, `audio` `layers`
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`video` still empty because the run had not reached those stages.
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- stage times: crop 85s, vision ~4min, identity ~1min, reconcile ~7min, dialogue ~8min.
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- 24 of 81 speech lines resolve to a named character, 30%.
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- 26 of 113 detected people got an identity, 23%, and 25 of the 26 went to one character.
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- 3 of 3 sampled two-character panels attribute both speakers to the wrong person.
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- 113 orchestrator tests pass. `worker_vision.py` self-check passes.
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- `dialogue` 116/116, `direct` 116/116, `scene` 116/116, `script` 116/116.
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- speech lines 95, named 9. Multi-character panels 0 of 40 by design. Single-character 9 of 55.
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- `speaker_method`: `unknown` 47, `model_solo` 28, `solo_prior` 8, `som_face` 7, `turn_taking` 5.
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- unresolved name refs 3, all `brown ponytail, green dress`, from a neighbouring panel in the same
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8-panel window. Was 24 of 51 before the fix.
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- identity assignments 26, of which `character_afa762` "Choi Haeseon" holds 25.
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- registry duplicates that block a correct bind: `seonho` matches 2 rows, `lim seonho` matches 2 rows.
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- buckets during `tts`: `panels` 116, `raw` 79, `manga` 491, `audio` 1, `layers` 0, `video` 0.
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## Open
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- The `script` stage failed at 87/116 with `unsupported-proper-noun: ['Choi', 'Haeseon']` on 28 beats.
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Not OOM, the worker stayed healthy. One-line fix named in
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`caveats/speaker-attribution.md#multiword-name-verifier`. Not applied.
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- The run stopped in `direct` at 113/116. `scene script tts layers render assemble` never ran. Resume:
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Since then it reached `scene` 116/116 and failed in `script`. Fix the verifier, then:
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- **The run is still going.** It was in `tts` at 26/116 when this was written, with `layers render
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assemble` unrun. `tts` is the bottleneck. Read it:
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```bash
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ssh kami@192.168.1.104 "curl -s -X POST http://127.0.0.1:9090/stage/clear -H 'Content-Type: application/json' -d '{\"job_id\":\"778297bc-e7ce-439d-91b5-8a027060d17f\",\"stage\":\"script\"}'"
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ssh kami@192.168.1.104 "curl -s -X POST 'http://127.0.0.1:9090/job/resume?job_id=778297bc-e7ce-439d-91b5-8a027060d17f'"
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/usr/bin/ssh kami@192.168.1.104 "curl -s 'http://127.0.0.1:9090/job/status?job_id=778297bc-e7ce-439d-91b5-8a027060d17f'"
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```
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- Speaker attribution is wrong in multi-character panels and the fix is not written
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(`caveats/speaker-attribution.md`). No code was changed for it.
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- Objects from the 2026-07-17 run still sit under `manga/<manga_id>/<chapter_id>/{pages,panels,audio,
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layers,clips}`. Nothing reads them. They are the rollback, not live data.
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- `rustfs` holds empty buckets. No mirror, no cutover.
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If it failed, clear the failed stage and resume:
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```bash
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/usr/bin/ssh kami@192.168.1.104 "curl -s -X POST http://127.0.0.1:9090/stage/clear -H 'Content-Type: application/json' -d '{\"job_id\":\"778297bc-e7ce-439d-91b5-8a027060d17f\",\"stage\":\"<stage>\"}'"
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/usr/bin/ssh kami@192.168.1.104 "curl -s -X POST 'http://127.0.0.1:9090/job/resume?job_id=778297bc-e7ce-439d-91b5-8a027060d17f'"
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```
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Note: plain `ssh` is the kitty ssh kitten and refuses non-interactive stdin. Use `/usr/bin/ssh`.
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- Defects 5 and 6 landed after `dialogue` had already run, so this run's 7 `som_face` lines are still
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labelled from the unpaired-mark path. The next dialogue pass fixes that. No name was affected.
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- The audit script is `docker exec manga-orchestrator python3 /tmp/audit_speakers.py`, source in this
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session's scratchpad. It is not in the repo. Copy it in if the metric is to be tracked per run.
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- The 3 cross-panel unresolved refs need the window's whole present-list, not one panel's.
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- Workers were restarted twice this session and are running in tmux `manga-workers`. Nothing watches
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them, and nothing watches the homesrv containers.
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@@ -153,3 +153,10 @@ Identity is now the binding constraint, not attribution. 26 of 113 detected peop
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23% named. The person who does hold an identity is stored as `Lim Seonho` while a separate row is named
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`Seonho` with alias `Lim Seonho`, so either name matches two rows, raises `ambiguous-speaker` and binds
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nothing.
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Two more defects surfaced after `dialogue` finished. All 7 `som_face` lines pointed at a mark whose face
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paired to no present character (`decisions/speaker-attribution.md#unpaired-mark`). Then `script` halted
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at 112/116 because the narrator wrote `"...Hm?"` for the source line `"Uh... hum...?"`, and both verifier
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rules fired on that two-letter interjection
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(`decisions/speaker-attribution.md#interjection-false-positive`). After the fix, `script` passed 116/116,
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the first time this chapter has cleared the verifier. `tts` then ran for the first time.
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@@ -0,0 +1,105 @@
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"""Speaker-attribution audit for one chapter. Runs inside manga-orchestrator (reads /data/manga.db).
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Answers the three questions asked of the 2026-08-11 rerun:
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1. what happens on a multi-character panel whose people have no identity,
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2. whether a known character gets mis-named,
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3. what the named-speaker share actually is now.
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"""
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import collections
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import json
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import sqlite3
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import sys
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CHAPTER = sys.argv[1] if len(sys.argv) > 1 else "7c944dd4-e972-42c7-ba60-9f6939548e80"
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SPEECH = {"speech", "shout", "thought"}
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c = sqlite3.connect("/data/manga.db")
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c.row_factory = sqlite3.Row
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manga_id = c.execute("SELECT manga_id FROM chapters WHERE chapter_id=?", (CHAPTER,)).fetchone()[0]
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reg = {r["character_id"]: dict(r) for r in c.execute(
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"SELECT character_id, name, aliases, description FROM characters WHERE manga_id=?", (manga_id,))}
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# duplicate registry rows make a correct name unresolvable: an answer matching two rows is ambiguous.
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by_name = collections.defaultdict(list)
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for cid, r in reg.items():
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for n in [r["name"], *json.loads(r["aliases"] or "[]")]:
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if n:
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by_name[str(n).strip().casefold()].append(cid)
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dupes = {n: ids for n, ids in by_name.items() if len(ids) > 1}
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panels = c.execute("SELECT panel_id, panel_index, page_index FROM panels WHERE chapter_id=? ORDER BY \"order\"",
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(CHAPTER,)).fetchall()
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methods, kinds, per_char = collections.Counter(), collections.Counter(), collections.Counter()
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speech = named = 0
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crowded_speech = crowded_named = 0
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solo_speech = solo_named = 0
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unresolved_names = collections.Counter()
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ambiguous = []
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no_identity_crowd = 0
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# a line attributed to a real present local_id that simply has no identity row: attribution succeeded
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# and the name is lost anyway. This is the identity-coverage wall, not an attribution failure.
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attributed_but_unassigned = collections.Counter()
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for p in panels:
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pid = p["panel_id"]
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row = c.execute("SELECT result_json FROM vision_results WHERE panel_id=?", (pid,)).fetchone()
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if not row:
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continue
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v = json.loads(row["result_json"])
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if "dialogue" not in v:
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continue
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people = [ch for ch in (v.get("characters") or []) if ch.get("local_id")]
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assigned = {a["local_id"] for a in c.execute(
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"SELECT local_id FROM identity_assignments WHERE panel_id=?", (pid,))}
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crowd = len(people) > 1
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if crowd and not assigned:
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no_identity_crowd += 1
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for d in v["dialogue"]:
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if d.get("type", "speech") not in SPEECH:
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continue
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speech += 1
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ref = d.get("speaker_ref") or {}
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kind = ref.get("kind")
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methods[d.get("speaker_method")] += 1
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kinds[kind] += 1
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if crowd:
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crowded_speech += 1
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else:
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solo_speech += 1
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if kind == "character_id":
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named += 1
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per_char[reg.get(ref["value"], {}).get("name") or ref["value"]] += 1
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if crowd:
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crowded_named += 1
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else:
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solo_named += 1
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elif kind == "unknown":
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# normalize_dialogue nulls the flat `speaker` unless it resolved, but speaker_ref keeps the
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# unresolved local_id as its value.
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raw = str(ref.get("value") or "").strip()
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local_ids = {ch["local_id"] for ch in people}
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if raw in local_ids and raw not in assigned:
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attributed_but_unassigned[d.get("speaker_method")] += 1
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elif kind == "name":
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unresolved_names[ref.get("value")] += 1
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if ref.get("candidates"):
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ambiguous.append((pid, ref.get("value"), ref["candidates"]))
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print(f"panels with dialogue: {sum(1 for p in panels if (lambda r: r and 'dialogue' in json.loads(r['result_json']))(c.execute('SELECT result_json FROM vision_results WHERE panel_id=?', (p['panel_id'],)).fetchone()))}/{len(panels)}")
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print(f"speech lines: {speech} named (character_id): {named} = {100*named/max(speech,1):.0f}%")
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print(f" multi-character panels: {crowded_named}/{crowded_speech} named")
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print(f" single-character panels: {solo_named}/{solo_speech} named")
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print(f"multi-character panels with NO identity at all: {no_identity_crowd}")
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print(f"attributed to a present local_id with NO identity row: {dict(attributed_but_unassigned)}")
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print(f"speaker_method: {dict(methods)}")
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print(f"speaker_ref kind: {dict(kinds)}")
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print(f"named per character: {dict(per_char)}")
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print(f"unresolved name refs: {dict(unresolved_names)}")
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print(f"registry duplicate names (block a correct bind): "
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f"{ {n: [reg[i]['name'] for i in ids] for n, ids in dupes.items()} }")
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if ambiguous:
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print("ambiguous binds:")
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for pid, val, cands in ambiguous[:20]:
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print(f" {pid} {val!r} -> {[reg.get(x, {}).get('name') for x in cands]}")
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@@ -14,13 +14,17 @@ Workaround: none. Clear identity and rerun, which loses the good merges too.
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## Clearing a stage does not undo what it wrote {#dishonest-clearing}
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Dialogue and direction mutate the shared vision JSON. Clearing dialogue leaves its keys in place, so a
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rerun treats old dialogue as completed. Clearing identity preserves the per-manga registry.
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The dialogue and direct half of this is fixed and proven
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(`decisions/storage-layout.md#clear-vision-blob`). It cost a wasted rerun on 2026-08-11 first: the clear
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returned `{"ok": true}`, deleted nothing, and the stage skipped all 116 panels.
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What remains: clearing identity preserves the per-manga registry by design, so a rerun inherits every
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character it ever minted (`caveats/speaker-attribution.md#registry-pollution`). Nothing verifies that a
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clear emptied what it claimed.
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Costs: a rerun silently reuses stale output, which reads as a reproducible result.
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Revisit when: any stage is scheduled concurrently or resumed automatically. A stage must be idempotent
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before either is safe.
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Workaround: delete the keys by hand, or clear from `crop` down.
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## ComfyUI uses the GPU outside the session mutex {#comfyui-unscheduled}
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@@ -32,4 +32,7 @@ still live belongs in `caveats/`.
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| [RustFS is staged, not adopted](storage-layout.md#rustfs-staged) | open |
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| [A model guess is never labelled `tail`](speaker-attribution.md#no-fake-tail) | closed |
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| [The model's speaker answer is resolved against what the prompt showed](speaker-attribution.md#prompt-label-answers) | closed |
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| [An unpaired set-of-mark label grounds nothing](speaker-attribution.md#unpaired-mark) | closed |
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| [An interjection is not a name and not a misquote](speaker-attribution.md#interjection-false-positive) | closed |
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| [Cast names enter the verifier tokenized](speaker-attribution.md#multiword-cast-names) | closed |
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| [Clearing a stage strips the vision blob it wrote](storage-layout.md#clear-vision-blob) | closed |
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@@ -54,6 +54,33 @@ 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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## An unpaired mark grounds nothing {#unpaired-mark}
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**Closed.** `_set_of_mark` labels a detected face `unknown` when gated pairing
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(`decisions/audit-phase1.md#gated-face-pairing`) matched it to no present character. An answer pointing
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at such a mark identifies nobody, so it must not carry `som_face`, the highest-trust provenance.
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Evidence: all 7 `som_face` lines in the first 36 panels of the 2026-08-11 rerun had `speaker_ref` kind
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`unknown`. The label sat on lines with no speaker. Same defect class as the fake `tail`.
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Forbids: deriving a provenance label from the label map without checking what the label resolved to.
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Check: `python worker_vision.py`, the `unpaired` case.
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## An interjection is not a name and not a misquote {#interjection-false-positive}
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**Closed.** `verify_script` must stay quiet on valid narration, because `run_stage_script` retries once
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and then raises (`decisions/audit-phase1.md#verifier-false-positives`).
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Evidence: the narrator wrote `"...Hm?"` for the source line `"Uh... hum...?"`. Both rules fired at once.
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`Hm` was absent from the source words, and a 6-character quote needs 5 matching characters to ground,
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so it got 4. That halted the `script` stage at 112/116 on job `778297bc`.
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Interjections join `_STOPWORDS`. A quote of three letters or fewer is no longer grounded-checked. That
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holds the same line as the dialogue prompt's 1-3 character noise rule. The stage then passed 116/116.
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Forbids: scoring a quote too short for the ratio to carry meaning.
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Check: `pytest test_script_verify.py`, `test_an_interjection_is_not_a_name_or_a_misquote`.
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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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@@ -56,3 +56,19 @@ instead. The check is not worth a failed start.
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write. Nothing is repointed. Two things still block a cutover, and neither is settled. RustFS is
|
||||
`1.0.0-beta.12`, labeled `build-type=prerelease`. Swapping storage also adds a variable to the run
|
||||
meant to produce the baseline. Task [#116].
|
||||
|
||||
## Clearing a stage strips the vision blob it wrote {#clear-vision-blob}
|
||||
|
||||
**Closed. 2026-08-11.** `dialogue` and `direct` have no output table. They write onto the per-panel
|
||||
vision blob, and `_STAGE_TABLES` had no entry for either, so `/stage/clear dialogue` deleted nothing and
|
||||
still returned `{"ok": true}`. `run_stage_dialogue` then saw `"dialogue" in vision` and skipped all 116
|
||||
panels.
|
||||
|
||||
Evidence: the first clear reported `scripts: 46, scene_graphs: 116` and no vision counts. After the fix
|
||||
the same call reported `vision_results.dialogue: 116` and `vision_results.direct: 75`, all of which the
|
||||
first clear had left in place. A whole rerun was wasted on stale data before this was found.
|
||||
|
||||
`_STAGE_VISION_KEYS` in `db.py` names the keys each stage owns, and `clear_stage_data` strips them.
|
||||
|
||||
Forbids: adding a stage that writes onto a shared blob without listing its keys there.
|
||||
Check: `pytest test_db.py`, `TestClearStageData`.
|
||||
|
||||
Reference in New Issue
Block a user