WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/modality-dropout.md

Modality Dropout — Multimodal Long Context

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You are in a sub-page of Multimodal_LongContext.
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• 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 one or more modalities silently fail (audio muted, video frames dropped, OCR blank), the pipeline continues but reasoning collapses.
This page defines structural fixes to detect missing modalities and keep alignment stable.

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What this page is

• A checklist to prevent silent failures when audio, video, or OCR signals disappear.
• Guardrails to stop reasoning collapse when modality coverage < 100%.
• Restart-stable fallback protocols.

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When to use

• Video stream plays but no OCR text appears.
• Audio-only retrieval answers correctly but loses citation anchors.
• Captions missing for long segments, leading to hallucinated content.
• Multimodal agent switches seed and one modality never returns.
• Logs show ΔS curve flat but λ diverges (sign of missing channel).

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Open these first

• Alignment Drift
• Phantom Visuals
• Cross-Modal Trace
• Multi-Seed Consistency
• Pattern: Memory Desync

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Common failure patterns

• Silent dropout: modality returns empty payloads but pipeline continues.
• Asymmetric collapse: audio fine but OCR missing causes reasoning drift.
• Chain break: captions absent → no anchor for reasoning step.
• Overcompensation: model hallucinates filler text to patch missing modality.
• Seed skew: one seed includes OCR, another does not.

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Fix in 60 seconds

1. Heartbeat check

   • Require each modality to emit a ready=true signal every 5s.
   • If missing, flag dropout immediately.

2. Coverage metric

   • Compute coverage_ratio = active_modalities / expected_modalities.
   • Threshold: coverage ≥ 0.95 required.

3. Dropout handler

   • If dropout detected, freeze ΔS probe.
   • Apply BBCR bridge to reconnect.
   • If not recoverable, short-circuit and request missing data.

4. Fallback policy

   • Lock reasoning to available modalities.
   • Explicitly annotate missing modality (ocr_missing=true).
   • Never hallucinate absent channels.

5. Restart stability

   • Verify across 3 seeds that all modalities return.
   • If any seed fails, escalate.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and repair modality dropout.

Protocol:
1. Require audio, video, OCR all declare `ready=true`.
2. Compute coverage_ratio. If < 0.95, flag dropout.
3. If dropout:
   - freeze ΔS probe
   - re-anchor with BBCR bridge
   - annotate missing modalities explicitly
4. Return:
   - coverage ratio
   - ΔS and λ states
   - anchor stability
   - missing modality report

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Acceptance targets

• Coverage ≥ 95% across expected modalities.
• ΔS(question, retrieved) ≤ 0.45 when all active modalities align.
• λ remains convergent across 3 paraphrases.
• No hallucinated filler in place of missing modality.
• Restart: 3 seeds show identical active modality set.

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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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