🧭 Quick Return to Map
You are in a sub-page of Multimodal_LongContext.
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- 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 multimodal pipelines rely on anchor tokens (timestamps, bounding boxes, snippet IDs) to align across modalities, drift can cause one stream to advance while the others lag. The model then reasons on mismatched anchors, producing hallucinations or misplaced grounding.
- Video timeline shows
t=5sanchor, but captions are aligned tot=4.2s - OCR snippets cite bounding box A, while speech transcripts cite bounding box B
- Long context replay produces flip-flop alignment across runs
- Answer references correct content but wrong time or position
- Clock skew: audio vs. video vs. text not normalized before indexing
- Buffer flush misalignments: truncated chunks shift anchors mid-window
- Asynchronous retrieval: one retriever returns stale anchor metadata
- Join collisions: overlapping chunks share same anchor ID
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Normalize clocks
- Round timestamps to fixed interval (e.g. 100ms).
- Ensure OCR/page anchors share same epoch.
-
Fence joins
- Enforce
{anchor_id, start, end}triplet. - Forbid overlapping
anchor_idacross modalities.
- Enforce
-
Stabilize variance
- Apply BBAM clamp when ΔS(anchor_i, anchor_j) > 0.55
- If collapse detected, re-anchor with BBCR bridge.
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Trace every step
- Require all outputs to cite
{anchor_id, modality, confidence}. - Drop responses with missing or conflicting anchors.
- Require all outputs to cite
- ΔS(anchor alignment) ≤ 0.45
- λ remains convergent across 3 runs with shuffled seeds
- Coverage ≥ 0.70 with consistent anchor IDs across modalities
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| 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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