WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/caption-collapse.md

Caption Collapse — Multimodal Long Context

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You are in a sub-page of Multimodal_LongContext.
To reorient, go back here:

• Multimodal_LongContext — long-context reasoning across text, vision, and audio
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

When captions or annotations break down under long windows, multimodal pipelines lose alignment and factual grounding.
This page focuses on stabilizing caption integrity for images, videos, and diagrams in extended sessions.

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What this page is

• A diagnostic page for caption degradation in multimodal contexts.
• Structural guardrails to keep captions aligned with visual evidence.
• Acceptance targets for ΔS and λ across captions and snippets.

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When to use

• Image captions are accurate at the start but drift after 20k+ tokens.
• Captions compress multiple regions into one vague statement.
• Video scene captions skip events or merge distinct frames.
• Diagram labels appear but reasoning no longer references them correctly.
• Captions look fluent but introduce objects not in the frame.

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Open these first

• Entropy Collapse
• Context Drift
• Retrieval Traceability
• Data Contracts
• Chunking Checklist

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Common failure patterns

• Vague compression: distinct objects collapsed into one generic caption.
• Temporal merge: video events blended into a single description.
• Phantom detail: captions include invented objects or properties.
• Citation loss: captions lack bounding boxes or region ids.
• Context slip: captions reference prior images instead of the current one.

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Fix in 60 seconds

1. Stamp captions with anchors

   • Add {region_id | bbox | frame_time} to each caption.
   • Require ΔS(caption, region) ≤ 0.45.

2. Enforce one-to-one mapping

   • Each object/region must have a unique caption line.
   • Forbid merges without explicit evidence.

3. Normalize caption schema

   • Require {subject | attribute | action} fields.
   • Disallow free-form hallucinations.

4. Clamp entropy

   • Apply BBAM when variance rises across caption tokens.
   • Apply BBCR bridge if captions diverge from visual anchors.

5. Trace joins

   • Log {region_id, caption_id, ΔS, λ_state}.
   • Fail if any caption has no visual anchor.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Stabilize captions across long multimodal contexts.

Steps:
1. Print each caption with {region_id | bbox | frame_time}.
2. Require cite-then-caption, forbid phantom objects.
3. Compute ΔS(caption, region). If ≥ 0.60, propose fix with data-contracts or chunking-checklist.
4. Apply BBAM if entropy rises. Apply BBCR if λ diverges.
5. Return: {Caption Table, Alignment Log, Final Answer}.

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Acceptance targets

• ΔS(caption ↔ region) ≤ 0.45
• Each caption tied to a valid region_id or frame_time
• λ remains convergent across three paraphrases
• No phantom objects or invented attributes
• Captions maintain one-to-one mapping with objects

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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