Multi-Hop Collapse — Multimodal Long Context

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

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

Hop schema lock
- Require {hop_id, input_modality, output_modality, snippet_id, ΔS} for each step.
- Forbid skipping hops.
ΔS checkpoints
- Compute ΔS at each hop transition.
- Threshold: ΔS ≤ 0.45 is stable, 0.45–0.60 transitional, ≥ 0.60 collapse risk.
λ continuity probe
- Record λ across hops: retrieval → fusion → reasoning.
- If λ flips divergent, apply BBAM clamp.
BBCR bridge
- Insert bridge node for missing or weak hop.
- Re-anchor using prior modality context.
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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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