manga-recap-pipeline/ARCHITECTURE.md

ARCHITECTURE

The target shape of the pipeline, written 2026-08-13 from the user's design. This is not what the code does. NEXT.md holds the live state and AUDIT.md holds the current pipeline. Every section here ends with what exists today, so the gap is legible without reading both.

The governing principle:

Do not make the next panel understand the previous panel. Make it understand the current world state produced by all previous panels.

Vision produces observations. A persistent chapter graph owns identity and relationships. Everything below follows from that split.

1. The page is a region graph, not a list of panels

page
 ├─ regions
 │   ├─ panel
 │   ├─ inset_panel
 │   ├─ embedded_art
 │   ├─ text
 │   ├─ tail
 │   └─ character_occurrence
 │
 └─ edges
     ├─ contains(region, region)
     ├─ reads_before(text, text)
     ├─ tail_of(tail, text)
     ├─ points_to(tail, character)
     ├─ spoken_by(text, character)
     └─ same_identity(character, character)

A flat set of panels cannot express a television inside a room. That is the defect the current pipeline shows most often.

Today: the crop stage emits a flat panel list with a bbox each, plus context_fragments, a non-destructive caption-to-face link. Vision emits per-panel characters and dialogue. There is no containment edge, no tail region and no region type.

2. Identity exists independently of names

occurrence c42
    -> identity char_07
         name = null
         aliases = []

char_07.name may be filled later, or stay null forever and display as unknown character #7. The occurrence is the observation, the identity is the cluster, the name is an optional label on the cluster. Three levels, never collapsed into one.

Today: the schema already has this split. identity_assignments is the occurrence, characters owns the identity, name is nullable and downstream already falls back to an anonymous display. What is missing is the clustering, not the separation. See section 4.

3. Speaker attribution is a scored graph edge, not a procedure

Do not write find bubble -> find tail -> nearest character. Score every plausible edge:

score(text, character) =
    learned_t2c_score
  + tail_evidence
  + spatial_evidence
  + same_panel
  + dialogue_continuity
  + character_activity_prior
  + identity_context

learned_t2c_score is the load-bearing term: a pair classifier over the whole page, the text object's visual feature and the character object's visual feature. Magi's text-character head does exactly this. It can start as a tiny MLP:

t2c(text_embedding, character_embedding, page_context, geometry_features) -> p(speaker)

with geometry carrying normalized relative position, distance, overlap, same-panel, containment depth and tail direction.

Then the cases fall out of one mechanism instead of four:

case	what carries it
bubble with a tail	t2c + tail, usually decisive
bubble with no tail	t2c + spatial and context
speaker outside the panel	recent identities + an offscreen candidate
narration	the narrator candidate
nothing resolves	unknown speaker

A dialogue line must not be required to resolve to a visible character. That is a failure mode, not a safeguard. The speaker type is a union:

speaker = visible(character_id) | offscreen(character_id?) | narrator | unknown

Today: speaker_ref is already a typed union of character_id | name | unknown | narrator (decisions/audit-phase1.md#speaker-ref-is-canonical). offscreen is the missing arm. Attribution is a prompt to gemma over a window of panels, with no geometry term at all. The det/seg tail heads exist and are unused (caveats/speaker-attribution.md#tail-is-not-geometry).

4. Character recognition is occurrence, then identity, then name

character detection
       ↓
occurrence embeddings
       ↓
pairwise same_identity probabilities
       ↓
chapter-wide constrained clustering
       ↓
char_001, char_002, ...
       ↓
optional character-bank lookup
       ↓
name or unknown

Two rules that the current code gets wrong.

The embedding is not the character crop alone. Combine four signals: the character crop, the face or head crop, the full-body crop, and a contextual object feature. Magiv2 combines detected object features with a separate crop-embedding model.

Cluster chapter-wide, not page by page.

Two characters in the same panel may be one person. Mirrors, photographs, flashbacks, insets, screens, imagined scenes and repeated action drawings all break that rule. Make it a weak cannot-link, and only when the two are on the same narrative plane.

Today: the embedding is the person box only, which is measurably the wrong signal (caveats/audit-open.md#cosine-not-identity). Clustering is greedy and local: tracklets.link_tracklets groups within an 8-panel window. tracklets.cannot_link treats same-panel co-presence as a hard constraint, which is exactly the correction above. Naming is db.add_name_claim, corroboration over name_claims.

5. The art-in-art problem needs a narrative plane

Treat the page as a hierarchical scene graph:

page
└── panel A                     depth=0
    ├── character c1
    ├── text t1
    └── television/poster       depth=1, type=embedded_art
        ├── character c2
        └── text t2

Speaker candidates normally come from the same scene_depth. Otherwise a real character standing beside a poster of a drawn person can be given the poster person's line.

A region classifier predicts a type:

story_scene | inset_story_panel | flashback | screen | photo | poster | illustration | decorative

Perfect classification is not the point. The output that matters is one probability:

same_narrative_plane(a, b)

which then enters the association score in section 3 and the cannot-link in section 4.

Today: nothing models this, and it is the whole of the remaining identity error on the lead. On the 19:44 run of 2026-08-12 his 16 assignments were 14 correct plus a photograph of another man and a chibi drawing. Both are art inside a panel. Vision also boxes cats as people and dresses them (p081, p108).

6. Narrative understanding is a state machine, not a per-panel description

story_state
├─ entities        (characters, locations, important objects)
├─ scenes
├─ timeline
├─ relationships
├─ unresolved_threads
├─ facts
└─ hypotheses

A panel produces a delta, not another standalone prose interpretation:

panel 142:
- character_07 enters room_03
- character_02 is already present
- character_07 says "..."
- object_12 changes owner: 02 -> 07
- possible flashback begins

Facts, hypotheses and unknowns are different records

fact:        source=panel_142  confidence=0.99  character_07 is visible
hypothesis:  confidence=0.64                    character_07 is angry
unknown:                                        who caused the explosion

A later panel strengthens, replaces or invalidates a hypothesis without rewriting history.

Scene state is explicit and inherited

scene_31:
  location: school_rooftop
  time: evening
  participants: {char_03: present, char_07: present, char_11: offscreen}
  pov: null
  narrative_mode: present
  parent_scene: null

A panel inherits this unless visual evidence overrides it. That alone kills a class of errors. A character absent for one panel has not left. A panel with no background has not changed location. A tail-less line keeps the offscreen participant as a candidate. A close-up still belongs to the scene.

Classify the transition, not just the panel

CONTINUE_SCENE | NEW_SCENE | LOCATION_CHANGE | TIME_SKIP | FLASHBACK_START
FLASHBACK_END | DREAM/IMAGINATION | POV_CHANGE | EMBEDDED_SCENE

EMBEDDED_SCENE is what stops a television's contents mutating the room around it:

scene_12 present
 ├─ panel 101
 ├─ panel 102
 └─ embedded scene_13 [television]
      ├─ panel-like region
      └─ char_19

Character state is written by a resolver, never by the vision model

char_07:
  known_names: [...]
  currently_at: room_03
  status: alive
  appearance_state: {clothes: school_uniform, injured: true}
  relationships: {char_02: friend?}
  last_seen: panel_142

The path is observation -> resolver -> state transition, and the resolver may reject an impossible update.

Conversation state is its own record

conversation_18:
  scene: scene_31
  participants: [char_02, char_07]
  last_speaker: char_07
  addressee: char_02
  topic: missing_key

This is the strongest available prior for a tail-less bubble. Given A: where did you put it? / ... / A: don't lie., turn-taking assigns the middle line with no visual evidence at all.

An unresolved reference survives instead of being forced

unknown_04:
  type: person
  descriptions: ["the man from yesterday", "silhouette in panel_58"]
  candidate_ids: {char_12: 0.55, char_19: 0.22}

Chapter 6 may reveal unknown_04 == char_12, and that identity back-propagates through the graph. The same applies to unnamed characters, pronouns, disguised characters, mysterious objects and unseen speakers.

Two memories

• Working narrative state: the current scene and the recent ones, in detail.
• Canonical long-term memory: compressed facts, not chapter summaries. char_07 learned that char_02 betrayed the group. object_04 is held by char_11. char_03 does not know char_07 survived.

A chapter boundary is a checkpoint, not a reset

chapter_checkpoint:
  persistent_entity_changes / relationship_changes / location and status changes
  newly established facts / unresolved questions / active plot threads / final scene state

Chapter n+1 starts from that. The detailed panel graph may be kept forever. Only five things load into the model: the current scene, the previous scene, the relevant character records, the active threads, and retrieved old facts.

A consistency checker runs after each scene and chapter

Seven checks. A dead character appearing normally. A character knowing a fact before learning it. An object owned by two people at once. A flashback never closed. A location jump with no transition. A speaker who was neither present nor offscreen. A name that conflicts with the identity graph. The model proposes corrections. The graph stays the source of truth.

Today: none of this exists. Each stage reads its predecessor's blob for one panel or one beat. recent is a rolling list of the last few dialogue lines and is the only carried state. Chapter boundaries are a reset. There is no fact-versus-hypothesis distinction anywhere, which is why narration asserts things no panel shows (NEXT.md item 6).

7. The staged version worth building

Do not recreate Magi's monolithic network first. Detection, vision and character embeddings already exist, so stage it:

page
 ↓
region detector          panels / nested regions / texts / characters / tails
 ↓
object feature extraction
 ↓
three pair models        character↔character (identity)
                         text→character      (speaker)
                         text→tail           (bubble structure)
 ↓
chapter graph
 ↓
global character clustering
 ↓
optional naming
 ↓
ocr + reading order
 ↓
dialogue stream

The VLM then judges only the ambiguous graph edges. It no longer rediscovers every character and dialogue relationship from raw pixels on every panel. Magi formulates detection and association as graph generation, which is why it beats a crop, OCR and nearest-character pipeline here.

What to take from this before the rewrite

Three items are cheap against the current code and pay immediately. They are entered in NEXT.md, not here.

1. same_narrative_plane, as a per-detection field. Vision already returns per-panel boxes. Add a plane or depth to a detection, set when the model says the figure sits inside a screen, poster, photo or drawing. That buys the containment edge with no detector. It is the whole of the remaining identity error on the lead, and it feeds every stage below.
2. Same-panel co-presence becomes a weak cannot-link. tracklets.cannot_link currently makes it hard. It needs item 1 first, because the plane is what makes the weak version safe.
3. offscreen as a fourth speaker_ref kind. The union already exists, the arm does not.

Sources

Magi and Magiv2 for the detection-and-association-as-graph-generation formulation, the text-character pair head, and the character bank of exemplar images plus names. Magiv3 for panels, texts, characters and tails with their associations, and for character grounding between textual descriptions and detected character regions.