🧭 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 audio, video, and text streams drift out of sync, reasoning collapses even if each modality looks fine in isolation.
This page defines guardrails to detect and repair temporal misalignment across long multimodal contexts.
- A structured fix for time drift in multimodal RAG and inference.
- Defines probes to measure sync quality across audio, visual, OCR, and metadata.
- Provides restart-stable alignment methods.
- Subtitles and video captions slip by a few seconds in long windows.
- OCR text aligns to the wrong frame batch.
- Audio queries answer correctly but cite misaligned video anchors.
- Two reruns with the same seed produce different offsets.
- Long reasoning chains flip context after 40–60 minutes of runtime.
- Alignment Drift
- Cross-Modal Bootstrap
- Cross-Modal Trace
- Multi-Seed Consistency
- Memory Desync Pattern
- Subtitle lag: transcript trails 1–2s behind video.
- Frame lead: OCR text fires before the visual frame is in place.
- Audio-video skew: alignment starts fine, then drifts over long runs.
- Restart variance: replays of the same clip yield different anchor offsets.
- Accumulated drift: each batch adds ~50–100ms error until collapse.
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Normalize time anchors
- Require all modalities to declare timestamps in milliseconds.
- Convert relative offsets into absolute epoch.
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Anchor hash & lock
- For each frame window, compute
{audio_hash, ocr_hash, frame_hash}. - Validate alignment with ΔS ≤ 0.45 between modalities.
- For each frame window, compute
-
Drift probe
- Every 30s, measure
Δt = |video_ts – audio_ts|. - Reject if Δt > 500ms.
- Every 30s, measure
-
Realign
- On drift, re-anchor with nearest transcript chunk.
- Use BBCR bridge if reasoning collapses.
- Apply BBAM to clamp variance.
-
Restart stability
- Require offsets identical within ±100ms across 3 seeds.
- Log ΔS curve to verify stable recovery.
You have TXT OS and the WFGY Problem Map.
Task: Repair multimodal time sync.
Protocol:
1. Collect all modalities with explicit timestamps.
2. Convert all offsets to absolute ms.
3. Compute Δt between audio, video, OCR anchors. If Δt > 500ms, flag drift.
4. Re-anchor captions to nearest visual frame.
- If collapse persists, apply BBCR and BBAM.
5. Return:
- Sync status
- Anchor hashes
- ΔS and λ states
- Corrected offsets- Δt ≤ 500ms across audio, video, OCR at all times.
- ΔS(question, retrieved) ≤ 0.45 for aligned anchors.
- λ remains convergent across 3 paraphrases.
- Restart stability: offsets identical within ±100ms across 3 seeds.
- No cumulative drift beyond 1s after 1h runtime.
| 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 |
| 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 |
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