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139 lines
6.6 KiB
Markdown
139 lines
6.6 KiB
Markdown
# Fusion Latency — Multimodal Long Context
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Multimodal_LongContext**.
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> To reorient, go back here:
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>
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> - [**Multimodal_LongContext** — long-context reasoning across text, vision, and audio](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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Multimodal models often **fuse audio, visual, and text streams** over long windows.
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If one modality lags behind during fusion (e.g., audio behind video, caption behind OCR),
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reasoning alignment collapses. This is **fusion latency** — the pipeline produces valid snippets
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but assembles them in the wrong temporal or semantic order.
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---
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## What this page is
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- A compact guide to detect and repair cross-modal latency.
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- Ensures audio, video, OCR, and text stay aligned at fusion points.
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- Provides ΔS and λ probes to measure synchronization drift.
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---
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## When to use
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- Video reasoning cites the correct frame but audio snippet lags a few seconds.
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- OCR text is valid but fused into the wrong moment of the transcript.
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- QA answers reference correct modalities but join them out of order.
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- Latency accumulates after multi-hop fusion (e.g., visual → text → audio).
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- Live streaming models show desync between captions and dialogue.
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---
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## Open these first
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- [Cross-Modal Trace](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Multimodal_LongContext/cross-modal-trace.md)
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- [Time-Sync Failure](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Multimodal_LongContext/time-sync-failure.md)
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- [Desync Amplification](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Multimodal_LongContext/desync-amplification.md)
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- [Sync Loop](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Multimodal_LongContext/sync-loop.md)
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---
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## Common failure patterns
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- **Audio lag** — transcript anchors drift a few seconds behind video frames.
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- **Visual lead** — bounding boxes arrive earlier than caption text.
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- **Cascade delay** — each hop (OCR → text → audio) adds small latency that compounds.
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- **Fusion mismatch** — correct snippets fused but in inverted order.
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---
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## Fix in 60 seconds
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1. **Timestamp normalization**
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- Require every snippet to carry `{start, end, modality}` in milliseconds.
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- Disallow fusion without temporal overlap check.
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2. **ΔS sync probe**
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- Compare ΔS(audio, video), ΔS(text, video), ΔS(OCR, audio).
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- Alert if ΔS ≥ 0.55 across adjacent streams.
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3. **λ stability check**
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- Log λ for each fusion step (modality pair → reasoning).
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- Divergence indicates sync skew.
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4. **Backpressure guard**
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- If one modality lags, buffer others until ΔS < 0.50.
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- Apply BBCR to re-anchor fused streams.
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5. **Re-trace**
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- If fusion collapse occurs, re-run cross-modal trace with alignment locks.
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- Require new citations before producing final answer.
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---
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## Copy-paste prompt
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```txt
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You have TXT OS and the WFGY Problem Map.
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Task: Detect and repair multimodal fusion latency.
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Steps:
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1. List all snippets with {modality, start, end, offsets}.
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2. Compute ΔS across all adjacent modalities.
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3. If ΔS ≥ 0.55, buffer or re-align streams.
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4. Apply BBCR bridge if collapse occurs.
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5. Output corrected fused chain with timestamps and ΔS values.
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````
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---
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## Acceptance targets
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* ΔS(modality\_i, modality\_j) ≤ 0.45 across all fusions.
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* λ remains convergent at fusion and reasoning stages.
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* No compounded latency across >3 hops.
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* All citations aligned within ±250ms temporal skew.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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