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

Multi-Hop Collapse — 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 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.

---

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.

---

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.”

---

Open these first

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

---

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.

---

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.

---

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

---

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.

---

🔗 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

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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.