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.

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.

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).

Open these first

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.

Fix in 60 seconds

Fingerprint each modality
- Compute embedding signatures separately for OCR, audio, caption, and visual frames.
- Verify signatures match expected modality domain.
Cross-modality consistency check
- Compare OCR tokens vs. caption words vs. audio transcript.
- If overlap >50%, suspect swap.
Re-map against anchor modality
- Use video timeline or gold reference as ground truth.
- Re-align swapped data to the correct channel.
Apply BBPF + BBCR
- Use BBPF (pathfinder) to re-thread the swapped modality.
- Use BBCR bridge to anchor swapped stream back into context.
Fence schema
- Enforce explicit modality tags {OCR|Caption|Audio|Visual} in contracts.
- Forbid ingestion without modality ID.

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

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.

🔗 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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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