Cross-Modal Trace

🧭 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 text, image, or video claims cannot be traced to an anchor, drift accumulates and phantom evidence appears.
This page defines the schema and checks that guarantee traceability across all modalities.

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What this page is

• Schema rules for linking text answers back to visual or audio anchors.
• Audit trail structure to prevent orphan claims.
• Quick checks for reproducibility across long multimodal contexts.

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When to use

• Citations appear but no frame, region, or timestamp is attached.
• Audio transcript and video caption disagree about speaker or object.
• Text answers cite nonexistent visuals or wrong timecodes.
• Multi-day multimodal sessions lose track of the original anchor evidence.
• OCR captions or ASR transcripts collapse after long context windows.

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Open these first

• Phantom Visuals
• Caption Collapse
• Alignment Drift
• Retrieval Traceability
• Data Contracts

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Common failure patterns

• Orphan claims: answer references content without frame_id or timestamp.
• Cross-modal mismatch: transcript says "dog" while video shows only a cat.
• Anchor drift: same frame cited differently across runs.
• Phantom evidence: answer claims diagrams, sounds, or visuals never uploaded.
• Trace break: anchor IDs vanish during long-session context merging.

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Fix in 60 seconds

1. Enforce anchor schema

   • Require {frame_id | region_id | timestamp | source_id} for all claims.
   • Forbid free-text references without anchors.

2. Trace Table

   • Build a table with {claim | anchor_id | snippet | modality}.
   • Each row must be auditable.

3. ΔS probe

   • Compute ΔS(text, anchor).
   • Require ≤ 0.45 for stability. Reject claims if ≥ 0.60 and no match exists.

4. Cross-modal reconciliation

   • Compare text vs audio vs vision anchors.
   • If mismatch, flag as drift and escalate.

5. Audit trail log

   • Keep trace_id, mem_rev, anchor_refs.
   • Verify reproducibility across three paraphrases.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Enforce cross-modal traceability.

Protocol:
1. Require every claim to cite {frame_id, region_id, timestamp, source_id}.
2. Build a Trace Table with {claim, anchor_id, snippet, modality}.
3. Report ΔS(text, anchor). Reject phantom if ΔS ≥ 0.60 with no match.
4. Compare across modalities. If mismatched, return “cross-modal drift”.
5. Return {Trace Table, ΔS log, λ states, Final Answer}.

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Acceptance targets

• Every claim has a valid anchor ID.
• ΔS(text, anchor) ≤ 0.45.
• λ remains convergent across paraphrases.
• No phantom evidence across seeds.
• Trace Table reproducible across three runs.

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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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