Spatial Fusion Error

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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 spatial information from different modalities (text, image, video, 3D layout) is fused incorrectly,
the model builds a distorted scene map. This results in answers that are locally fluent but spatially wrong.

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What this page is

• A repair map for spatial mis-fusion across long multimodal windows.
• Structural checks that keep anchors aligned in 2D/3D space.
• Copy-paste prompts to enforce spatial traceability in multimodal RAG.

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When to use

• Text says "object A is left of object B" but visual encoder aligns them oppositely.
• Bounding boxes overlap or merge, losing spatial independence.
• 3D → 2D projection mismatch: captions reference an object that isn’t in frame.
• Video QA drifts: same entity appears in different spots across time.
• Answers mention correct objects but wrong spatial relations (left/right, inside/outside, above/below).

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Open these first

• Anchor Misalignment
• Visual Anchor Shift
• Cross-Modal Trace
• Reference Bleed

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Common failure patterns

• Axis flip — left/right or up/down swapped.
• Projection drift — 3D object references collapse to wrong 2D bounding box.
• Overlap collapse — two entities share the same spatial slot.
• Cross-modal mismatch — text anchor doesn’t correspond to visual bounding box.

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Fix in 60 seconds

1. Spatial schema lock

   • Represent anchors as {id, coords(x,y,z), frame_id}.
   • Reject answers missing explicit spatial schema.

2. ΔS probe across modalities

   • Compute ΔS(text_anchor, visual_anchor).
   • If ΔS ≥ 0.60, suspect fusion error.

3. Spatial IoU check

   • Enforce IoU ≥ 0.7 for same anchor across modalities.
   • If < 0.7, assign new anchor ID.

4. Stabilize with BBCR

   • Bridge text ↔ visual mismatch with constraint re-anchoring.
   • Clamp variance with BBAM.

5. Trace audit

   • Log {anchor_id, modality, coords, IoU}.
   • Require cite-then-answer with explicit anchor IDs.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and repair spatial fusion errors across modalities.

Steps:
1. Verify each anchor has {id, coords(x,y,z), frame_id}.
2. Compute IoU across modalities. If IoU < 0.7, treat as mismatch.
3. Probe ΔS across text and visual anchors.
4. Apply BBCR if drift detected, else assign new ID.
5. Return:
   - stable anchors
   - mismatched anchors
   - ΔS values and λ states
   - corrected spatial map

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Acceptance targets

• 100% anchors represented with explicit {id, coords, frame_id}.
• Cross-modal IoU ≥ 0.7 after fix.
• ΔS(text, visual) ≤ 0.45.
• λ remains convergent across paraphrases.
• No axis flip errors across test prompts.

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🔗 Quick-Start Downloads (60 sec)

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

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Explore More

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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