WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext
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Multimodal & Long-Context — Global Fix Map

A compact hub to stabilize reasoning and retrieval when multiple modalities (text, vision, audio, structured signals) interact across long contexts. Use this folder when models drift, collapse, or desync under multimodal fusion, large windows, or cross-sequence memory.


What this page is

  • A structural map of failure patterns unique to multimodal long-context use.
  • Each failure gets its own page with symptoms, root cause, and WFGY guardrails.
  • Works without infra changes — guardrails and schema fixes only.
  • Acceptance targets (ΔS, λ, E_resonance) are measurable and reproducible.

When to use

  • Model drifts when text and vision anchors must align over >50k tokens.
  • Citations or captions collapse when context grows.
  • Visual snippets appear but misalign with cited text.
  • Multi-hop reasoning mixes modalities but loses semantic anchor.
  • Long chains flip answers when fusing embeddings from multiple sources.

Common failure patterns


Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • ΔS across modality joins ≤ 0.50
  • Coverage ≥ 0.70 for intended anchors
  • λ remains convergent across three paraphrases and two modality-seeds
  • E_resonance stable across textvisionaudio triads

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1 Download · 2 Upload to your LLM · 3 Ask “Answer using WFGY + <your question>”
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🧭 Explore More

Module Description Link
WFGY Core WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack View →
Problem Map 1.0 Initial 16-mode diagnostic and symbolic fix framework View →
Problem Map 2.0 RAG-focused failure tree, modular fixes, and pipelines View →
Semantic Clinic Index Expanded failure catalog: prompt injection, memory bugs, logic drift View →
Semantic Blueprint Layer-based symbolic reasoning & semantic modulations View →
Benchmark vs GPT-5 Stress test GPT-5 with full WFGY reasoning suite View →
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