WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/fusion-latency.md

Fusion Latency — Multimodal Long Context

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Multimodal models often fuse audio, visual, and text streams over long windows.
If one modality lags behind during fusion (e.g., audio behind video, caption behind OCR),
reasoning alignment collapses. This is fusion latency — the pipeline produces valid snippets
but assembles them in the wrong temporal or semantic order.

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What this page is

• A compact guide to detect and repair cross-modal latency.
• Ensures audio, video, OCR, and text stay aligned at fusion points.
• Provides ΔS and λ probes to measure synchronization drift.

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When to use

• Video reasoning cites the correct frame but audio snippet lags a few seconds.
• OCR text is valid but fused into the wrong moment of the transcript.
• QA answers reference correct modalities but join them out of order.
• Latency accumulates after multi-hop fusion (e.g., visual → text → audio).
• Live streaming models show desync between captions and dialogue.

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Open these first

• Cross-Modal Trace
• Time-Sync Failure
• Desync Amplification
• Sync Loop

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Common failure patterns

• Audio lag — transcript anchors drift a few seconds behind video frames.
• Visual lead — bounding boxes arrive earlier than caption text.
• Cascade delay — each hop (OCR → text → audio) adds small latency that compounds.
• Fusion mismatch — correct snippets fused but in inverted order.

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Fix in 60 seconds

1. Timestamp normalization

   • Require every snippet to carry {start, end, modality} in milliseconds.
   • Disallow fusion without temporal overlap check.

2. ΔS sync probe

   • Compare ΔS(audio, video), ΔS(text, video), ΔS(OCR, audio).
   • Alert if ΔS ≥ 0.55 across adjacent streams.

3. λ stability check

   • Log λ for each fusion step (modality pair → reasoning).
   • Divergence indicates sync skew.

4. Backpressure guard

   • If one modality lags, buffer others until ΔS < 0.50.
   • Apply BBCR to re-anchor fused streams.

5. Re-trace

   • If fusion collapse occurs, re-run cross-modal trace with alignment locks.
   • Require new citations before producing final answer.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and repair multimodal fusion latency.

Steps:
1. List all snippets with {modality, start, end, offsets}.
2. Compute ΔS across all adjacent modalities.
3. If ΔS ≥ 0.55, buffer or re-align streams.
4. Apply BBCR bridge if collapse occurs.
5. Output corrected fused chain with timestamps and ΔS values.

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Acceptance targets

• ΔS(modality_i, modality_j) ≤ 0.45 across all fusions.
• λ remains convergent at fusion and reasoning stages.
• No compounded latency across >3 hops.
• All citations aligned within ±250ms temporal skew.

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🧭 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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