WFGY/ProblemMap/GlobalFixMap/Eval_Observability/lambda_observe.md

Eval Observability — λ_observe

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Evaluation disclaimer (λ_observe)
λ_observe is a heuristic stability signal defined inside the WFGY framework.
It helps you notice drift and volatility but is not a formal guarantee of long term robustness.

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A core probe for evaluating semantic convergence across multiple seeds, paraphrases, and retrieval variations.
While ΔS measures semantic distance, λ_observe captures stability vs divergence of reasoning paths.

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Why λ_observe matters

• Detect fragile reasoning: Even when ΔS looks safe, λ divergence indicates unstable chains.
• Identify paraphrase sensitivity: If λ flips across harmless rewordings, the system is brittle.
• Audit retrieval randomness: Different seeds producing opposite λ signals reveal weak schema.
• Ensure eval reproducibility: Stable λ means tests repeat reliably under small perturbations.

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λ state encoding

Symbol	Meaning	Example failure
→	Forward convergence, stable path	Same citations and reasoning across paraphrases
←	Backward collapse, early abort	Tool call retries, empty citations
<>	Split state, partial divergence	One paraphrase cites correct snippet, others miss
×	Total collapse	Random answers, no citation alignment

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Acceptance targets

• Convergence rate ≥ 0.80 across 3 paraphrases × 2 seeds.
• No × states tolerated in gold-set eval.
• Split states (<>): ≤ 10% of test cases acceptable.
• Forward (→) must dominate stable runs.

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Evaluation workflow

1. Run triple paraphrase probe
   Ask the same question three ways. Collect λ states.
2. Repeat with two seeds
   Track variance.
3. Roll-up stats
   Compute convergence ratio, collapse frequency, divergence rate.
4. Escalation
   If λ <0.80 or × >0%, run root-cause: schema audit, retriever split, prompt ordering.

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Example probe schema

{
  "query_id": "Q42",
  "runs": [
    {"paraphrase": 1, "seed": 123, "λ": "→"},
    {"paraphrase": 2, "seed": 123, "λ": "→"},
    {"paraphrase": 3, "seed": 123, "λ": "<>"},
    {"paraphrase": 1, "seed": 456, "λ": "→"},
    {"paraphrase": 2, "seed": 456, "λ": "×"},
    {"paraphrase": 3, "seed": 456, "λ": "→"}
  ]
}

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Common pitfalls

• Only measuring ΔS → misses hidden divergence.
• Seed-fixed eval → looks stable but fragile in production.
• Ignoring split states → small divergence often grows into collapse.
• No per-query logs → averages hide catastrophic single failures.

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Reporting recommendations

• λ distribution table: % of →, ←, <>, ×.
• Convergence trend: chart over time by eval batch.
• Drift alerts: trigger if convergence <0.80 or × appears.
• Correlation: track ΔS vs λ to spot mixed failures.

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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 + <your question>”
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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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