Eval: Operator Guidelines

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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This page sets strict rules for human operators and evaluators running WFGY-aligned evaluation suites. Evaluation is not a free-form activity. It must follow consistent contracts, logging standards, and reproducible steps.

Open these first

Gold set design: goldset_curation.md
Precision/recall evals: eval_rag_precision_recall.md
Observability probes: alerting_and_probes.md

Acceptance targets

Inter-annotator agreement ≥ 0.85 across 3 operators
Annotation variance ≤ 10%
Audit reproducibility ≥ 95% (rerun matches logged label)
No operator-induced drift: ΔS stability remains within ±0.05 after evaluation

Operator responsibilities

Stay schema-locked Every operator must apply the same definition of "correct" → answer cites the correct snippet and explains consistently.
Use three-paraphrase rule Rephrase the query 3× before marking coverage. If answer flips across paraphrases, log as unstable.
Log ΔS and λ Each annotated run must include structural metrics: ΔS(question,retrieved), λ states, snippet IDs.
Apply cost view Record token counts and cost-per-correct when judging pipelines.
No ad-hoc overrides Do not improvise “fixes” in evaluation logs. All fixes must point to an existing ProblemMap page.

Annotation protocol

Step 1: Load baseline Open gold set. Verify expected snippet IDs.
Step 2: Apply candidate system Run the same queries with candidate pipeline.
Step 3: Compare
- If candidate cites correct snippet and stays under ΔS ≤ 0.45 → mark correct.
- If citation missing or ΔS ≥ 0.60 → mark fail, attach probable ProblemMap fix.
Step 4: Log JSON

{
  "operator": "user123",
  "q_id": "Q42",
  "question": "What is the cutoff for semantic drift?",
  "expected_snippet": "S123",
  "candidate_snippet": "S123",
  "ΔS": 0.39,
  "λ_state": "→",
  "correct": true,
  "cost_usd": 0.0012,
  "notes": "Stable across 3 paraphrases"
}

When to escalate

Annotators disagree by >15% → escalate to eval lead.
ΔS drifts beyond thresholds but operators label "correct" → escalate and reconcile.
Costs exceed 1.5× baseline → halt run until reviewed.

Audit checklist

All logs stored in versioned JSONL.
At least 2 operators per eval run.
Spot check 10% of logs with independent reviewer.
Archive outputs with system hash, gold set version, and ProblemMap pointers.

🔗 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

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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