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

Module	Description	Link
WFGY Core	Canonical framework entry point	View
Problem Map	Diagnostic map and navigation hub	View
Tension Universe Experiments	MVP experiment field	View
Recognition	Where WFGY is referenced or adopted	View
AI Guide	Anti-hallucination reading protocol for tools	View

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