WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/reference-bleed.md

Reference Bleed — Multimodal Long Context

🧭 Quick Return to Map

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 anchor references from one modality bleed into another (e.g., text citations treated as video frame IDs, or audio timestamps mapped to OCR page offsets), the reasoning layer merges them incorrectly.
This is a subtle but destructive failure because each modality appears intact, yet the cross-modal references are poisoned.

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What this page is

• A repair guide for reference leakage across modalities.
• How to detect when anchors from one stream migrate into another.
• Structural guardrails to prevent false joins.

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When to use

• Captions include numeric anchors that actually come from OCR line numbers.
• Audio timestamps are reused as image frame references.
• Citations look correct individually, but do not map to their source modality.
• Fusion produces valid-looking answers that cite the wrong modality channel.
• Models drift into hallucination loops citing phantom anchors.

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Open these first

• Cross-Modal Trace
• Anchor Misalignment
• Desync Amplification
• Modality Swap

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Common failure patterns

• OCR bleed into captions — OCR line numbers reused as subtitle timestamps.
• Audio bleed into metadata — transcript anchors become page markers.
• Cross-join bleed — embeddings align across modality without guard, mixing references.
• Loop bleed — once references bleed, fusion propagates wrong anchors forward.

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Fix in 60 seconds

1. Tag and fence references

   • Enforce modality-specific IDs: {ocr_id, cap_id, aud_id, vis_id}.
   • Reject any anchor missing a modality tag.

2. Anchor validation

   • Cross-check anchor against source modality.
   • If caption ID not found in subtitle stream, discard.

3. ΔS probe on anchors

   • Compute ΔS(anchor, expected modality anchor).
   • If ≥0.60, suspect bleed.

4. Re-anchor with BBCR

   • Use BBCR bridge to reconnect reference to correct modality.

5. Audit trail

   • Require citation schema: {snippet_id | modality | offsets}.
   • Forbid references missing modality metadata.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and repair reference bleed across modalities.

Steps:
1. Verify modality tag on each anchor.
2. If tag mismatch, drop or re-map via BBCR.
3. Re-anchor using correct modality stream.
4. Output:
   - anchor table with modality tags
   - suspected bleeds
   - fixed mapping
   - ΔS and λ states

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Acceptance targets

• 100% anchors contain explicit modality tags.
• ΔS(anchor, expected modality) ≤ 0.45 after repair.
• λ remains convergent across paraphrases.
• No references propagate without modality validation.

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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 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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