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Caption Collapse — Multimodal Long Context

🧭 Quick Return to Map

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

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.

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.

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.

Open these first

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.

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.

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}.

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

🔗 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

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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