WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/multi-hop-collapse.md

Multi-Hop Collapse — Multimodal Long Context

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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

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When reasoning requires multi-hop steps across modalities (e.g., text → image → audio → video),
the chain often collapses midway. The model answers only the first hop or fabricates the rest,
losing alignment between evidence sources.

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What this page is

• A targeted fix for multi-hop multimodal reasoning failures in long-context sessions.
• Defines measurable checkpoints for each hop.
• Provides guardrails to keep ΔS and λ stable across chained modalities.

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When to use

• A video QA task asks: “What does the person say after showing the book?” → model answers book title but skips speech.
• An OCR pipeline extracts text, but reasoning ignores it in the final image caption.
• Chain-of-thought starts correctly, then jumps to a hallucinated answer without citing the second modality.
• Multi-step retrieval returns correct snippets, but only the first snippet is used.
• Answers flip between runs depending on which hop the model “forgets.”

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Open these first

• Cross-Modal Trace
• Modal Bridge Failure
• Anchor Misalignment
• Sync Loop

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Common failure patterns

• Single-hop truncation — only the first modality is processed, chain stops.
• Bridge collapse — second hop exists but produces null output or irrelevant data.
• Hallucinated completion — model skips missing modality and fabricates plausible link.
• Order inversion — hops are executed in the wrong sequence.

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Fix in 60 seconds

1. Hop schema lock

   • Require {hop_id, input_modality, output_modality, snippet_id, ΔS} for each step.
   • Forbid skipping hops.

2. ΔS checkpoints

   • Compute ΔS at each hop transition.
   • Threshold: ΔS ≤ 0.45 is stable, 0.45–0.60 transitional, ≥ 0.60 collapse risk.

3. λ continuity probe

   • Record λ across hops: retrieval → fusion → reasoning.
   • If λ flips divergent, apply BBAM clamp.

4. BBCR bridge

   • Insert bridge node for missing or weak hop.
   • Re-anchor using prior modality context.

5. Cite all hops

   • Require at least one snippet citation from each hop.
   • Stop output if any hop is missing evidence.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Repair multi-hop multimodal collapse.

Steps:
1. List all hops in the chain {hop_id, from_modality → to_modality}.
2. For each hop, compute ΔS and record λ state.
3. If ΔS ≥ 0.60 at any hop, re-run retrieval and insert BBCR bridge.
4. Output must include:
   - citations per hop
   - ΔS values
   - λ states
   - fused final reasoning

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Acceptance targets

• Every hop cited with snippet evidence.
• ΔS ≤ 0.45 at each hop boundary.
• λ remains convergent across three paraphrases.
• No fabricated hops or skipped modalities.

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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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