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

Modality Swap — Multimodal Long Context

When inputs from different modalities are accidentally swapped or misrouted (e.g., OCR output fed into caption pipeline, audio transcript aligned as video metadata), the entire reasoning chain collapses.
This is one of the hardest multimodal errors to debug because every modality appears syntactically correct, but the semantic anchors belong to the wrong channel.

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What this page is

• A focused repair guide for cross-modality swap failures.
• How to detect modality swaps early with structural probes.
• Guardrails to prevent silent misrouting across long-context pipelines.

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When to use

• Captions look valid textually but describe a different video segment.
• OCR transcript shows inside the "caption" stream, while actual captions disappear.
• Audio embeddings drift sharply while ΔS remains low inside the wrong modality.
• Model answers flip between describing visuals vs. reading text.
• Citations remain “correct” but belong to the wrong modality (e.g., frame vs. page).

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Open these first

• Cross-Modal Trace
• Anchor Misalignment
• Phantom Visuals
• Multimodal Fusion Break

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Common failure patterns

• OCR → Caption swap — OCR text injected as subtitle stream.
• Audio → Metadata swap — ASR transcript stored as document metadata.
• Caption → OCR swap — time-stamped captions parsed into wrong page references.
• Silent multimodal swap — all channels populated, but semantically wrong modality.
• Chained swap amplification — one early swap causes fusion to fail downstream.

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Fix in 60 seconds

1. Fingerprint each modality

   • Compute embedding signatures separately for OCR, audio, caption, and visual frames.
   • Verify signatures match expected modality domain.

2. Cross-modality consistency check

   • Compare OCR tokens vs. caption words vs. audio transcript.
   • If overlap >50%, suspect swap.

3. Re-map against anchor modality

   • Use video timeline or gold reference as ground truth.
   • Re-align swapped data to the correct channel.

4. Apply BBPF + BBCR

   • Use BBPF (pathfinder) to re-thread the swapped modality.
   • Use BBCR bridge to anchor swapped stream back into context.

5. Fence schema

   • Enforce explicit modality tags {OCR|Caption|Audio|Visual} in contracts.
   • Forbid ingestion without modality ID.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and correct modality swap in multimodal data.

Steps:
1. Fingerprint each modality stream.
2. Compare OCR vs Caption vs Audio for abnormal overlap.
3. If swap detected:
   - Re-map data back to correct modality based on anchors.
   - Apply BBPF + BBCR to stabilize reasoning.
4. Output:
   - modality checks
   - suspected swaps
   - corrected alignment
   - ΔS and λ states

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Acceptance targets

• No cross-modality overlap >20% except at validated joins.
• ΔS(question, retrieved) ≤ 0.45 after swap repair.
• λ convergent across paraphrases.
• Explicit modality tags enforced in every schema contract.
• Zero “silent swaps” propagate beyond first ingestion stage.

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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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