WFGY/ProblemMap/wfgy-metrics.md

📐 WFGY Metrics — Canonical Definitions

A single spec for measuring semantic accuracy, stability, cost, and safety across any LLM system.

Evaluation disclaimer (WFGY metrics)
This document defines a set of WFGY style metrics for inspecting model behavior and RAG pipelines.
These metrics are heuristic instruments that highlight patterns and failure modes in a given setup.
They do not by themselves prove that a system is safe, aligned or correct in all cases.
When you use these metrics, you should report the models, prompts, datasets and thresholds that were chosen and avoid treating any single score as a scientific guarantee.

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Why read this?
– You can’t improve what you can’t measure.
– ΔS, λ_observe, and E_resonance already power the Problem Map, Semantic Clinic, and WFGY’s CI templates.
– Standard names = instant compatibility with Grafana, Prometheus, LangSmith, Phoenix, and custom OTEL traces.

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0 · Metric Taxonomy

Pillar	Metric	Symbol / Field	Primary Use
Semantic	Semantic Stress	deltaS	Detect drift / wrong chunks
	Answer F1 / EM	f1, em	QA accuracy
Logic	Logic Vector	lambda	Convergence / divergence flag
	Residual Coherence	e_resonance	Slow entropy leaks
Efficiency	Cost per 1 k tokens	usd_k	Budget guard
	Latency p95 (ms)	latency_p95	SLO gate
Safety	Opcode / Tool Jailbreak	tool_offtrack	Router drift
	Citation Precision	cite_prec	Hallucination check

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1 · Formal Definitions

1.1 deltaS — Semantic Stress

ΔS = 1 − cos( I , G )

I = embedding of live text, G = embedding of expected ground/anchor.
Target bands: stable < 0.40 · transitional 0.40-0.60 · risk ≥ 0.60

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1.2 lambda — Logic Vector

λ ∈ {→ convergent, ← divergent, <> recursive, × chaotic}

Computed by PCA on consecutive embedding deltas; sign of first component.

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1.3 e_resonance — Residual Coherence

E = mean_t‖B_t‖, where B_t = I_t − G_t + m·c² (see BBMC).
Flat or downward trend = healthy; upward slope > 0.02 = entropy leak.

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2 · Reference Thresholds (production)

Metric	PASS	WARN	FAIL
deltaS_q_ctx	≤ 0.45	0.45 – 0.60	> 0.60
lambda	all →	← appears 1-2×	persistent ← / ×
e_resonance	slope ≤ 0	slope 0 – 0.02	slope > 0.02
cite_prec	≥ 0.90	0.80 – 0.90	< 0.80
usd_k	≤ baseline	+0 – 10 %	> 10 % jump
latency_p95	within SLA	1.2 × SLA	> 1.5 × SLA

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3 · Python Helper

from wfgy.metrics import deltaS, lambda_state, e_resonance

q   = "How do I renew my passport?"
ctx = rag_retrieve(q)
print("ΔS:", deltaS(q, ctx))           # 0.37

ans = llm_reason(ctx, q)
print("λ :", lambda_state(ans))        # →

print("E :", e_resonance())            # rolling avg

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4 · OpenTelemetry Mapping

# otel_map.yaml
deltaS:        wfgy.semantic.deltaS
lambda:        wfgy.logic.lambda
e_resonance:   wfgy.logic.e_res
usd_k:         wfgy.cost.usd_per_k
latency_p95:   wfgy.latency.p95

Any WFGY-instrumented app auto-emits these names; map others via the file above.

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5 · PromQL Alert Cookbook

- alert: SemanticDriftHigh
  expr: wfgy_semantic_deltaS > 0.60
  for: 1m
- alert: LogicVectorDivergent
  expr: wfgy_logic_lambda == 1   # 1 = divergent
  for: 2m
- alert: ResidualEntropyClimb
  expr: slope(wfgy_logic_e_res[15m]) > 0.02

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6 · CSV Schema (offline eval)

timestamp,id,set,question,deltaS_q_ctx,lambda,answer_f1,cite_prec,lat_ms,usd_k

Feed into wfgy-eval compare A.csv B.csv.

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7 · FAQ

Q : Do I need separate GPU passes to compute embeddings for deltaS? A : No. Use cached embeddings from retrieval; for answer ΔS, embed answer once after generation.

Q : Can I add BLEU, Rouge, or faithfulness scores? A : Yes—map them under wfgy.custom.*. WFGY dashboards auto-discover.

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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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