🧭 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 text, image, audio, or video streams drift apart in long windows, the fusion layer collapses and reasoning degrades.
This page focuses on detecting and repairing multimodal alignment failure.
- A structural fix map for cross-modal drift.
- Helps keep language, vision, and audio in sync across long sessions.
- Defines measurable acceptance targets for ΔS and λ between modalities.
- Image or video reference is ignored after 15k–50k tokens.
- Audio transcript aligns for the first minutes but drifts later.
- Model hallucinates objects not present in the visual stream.
- Cross-modal reasoning (e.g., Q&A about a chart) produces flat or wrong answers.
- Captions or OCR text do not match the actual frames.
- Late fusion drift: text reasoning ignores the latest visual input.
- Audio-text skew: transcript desync causes answers to lag behind the clip.
- Phantom alignment: the model cites a visual region that does not exist.
- Cross-modal flattening: distinct modalities are merged into a vague statement.
- Sequential decay: early multimodal anchors remain correct, late anchors collapse.
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Stamp each modality
- Text:
snippet_id, line_no - Vision:
region_id, bbox - Audio:
frame_time, speaker_id
- Text:
-
Cross-modal ΔS checks
- Require ΔS(text, vision) ≤ 0.45
- Require ΔS(text, audio) ≤ 0.45
-
Schema lock
- Enforce
{subject | attribute | source_modality}per entry. - Forbid mixing without anchors.
- Enforce
-
Clamp variance
- If λ flips between modalities, apply BBAM.
- If collapse persists, insert BBCR bridge nodes.
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Trace fusion table
- Log all modalities in one alignment table with ΔS values.
- Fail fast if any modality lacks anchor.
You have TXT OS and the WFGY Problem Map.
Task: Stabilize multimodal reasoning across long windows.
Steps:
1. Print alignment table {text_id, vision_id, audio_id, ΔS, λ_state}.
2. Require cite-then-fuse, forbid phantom regions or hallucinated objects.
3. If ΔS ≥ 0.60 across any pair, propose fix from data-contracts or alignment-drift.
4. Apply BBAM on drift, BBCR on collapse.
5. Return {Fusion Table, Anchor Log, Final Answer}.- ΔS across modalities ≤ 0.45
- λ remains convergent across three paraphrases
- Every caption / audio frame maps to at least one visual anchor
- No phantom alignments, no modality ignored
- Fusion remains stable for >50k tokens
| 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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