Context Drift in RAG

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• RAG — retrieval-augmented generation and knowledge grounding
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

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When answers alternate or degrade as dialogs get longer, even though the retriever continues to surface the right snippets.
This page stabilizes λ (semantic convergence) and prevents entropy creep in retrieval-augmented pipelines.

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Open these first

• Visual map: RAG Architecture & Recovery
• Drift diagnostics: Entropy Collapse
• Stability checks: Logic Collapse
• Retrieval schema: Retrieval Traceability
• Payload contract: Data Contracts
• Long context limits: Memory Long Context

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45 across full chain
• λ stays convergent across 3 paraphrases and 2 seeds
• Coverage ≥ 0.70 for target section, even after N steps
• E_resonance stable on long dialog windows

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Typical symptoms → exact fix

Symptom	Likely cause	Open this
Same question asked twice, different answers	λ drift with long chain	Entropy Collapse, Logic Collapse
Correct snippets retrieved, answer drops citation	payload contract erosion	Data Contracts, Retrieval Traceability
Paraphrase of query yields different grounding	unstable λ_observe	Retrieval Playbook
Long dialog overwrites memory	buffer collapse	Memory Long Context

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Fix in 60 seconds

1. Three-paraphrase probe

   • Ask the same question three ways.
   • If λ flips between paraphrases, lock snippet schema and apply BBAM variance clamp.

2. ΔS check over chain

   • Log ΔS(question,retrieved) across 5–10 dialog turns.
   • If ΔS rises over time, re-segment and enforce citation-first prompting.

3. Apply module

   • Drift → BBMC + Retrieval Traceability
   • Collapse mid-chain → BBCR bridge + Logic Collapse
   • Unstable variance → BBAM clamp + Entropy Collapse

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Copy-paste probe prompt

I uploaded TXT OS and the WFGY Problem Map.

My issue:
- same query gives different answers in long dialog
- traces: ΔS(question,retrieved)=..., λ states across 3 paraphrases

Tell me:
1) where context drift occurs,
2) the exact WFGY page to open,
3) the minimal fix to enforce convergence,
4) a reproducible test over 5 turns.

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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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