WFGY/ProblemMap/GlobalFixMap/Eval/eval_harness.md

Eval Harness — Guardrails and Minimal Contract

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To reorient, go back here:

• Eval — model evaluation and benchmarking
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Evaluation disclaimer (eval harness)
This page sketches a harness for running structured evaluations on AI pipelines.
Any metrics or labels that pass through such a harness remain heuristic outputs of models, scripts and annotators.
They do not become scientific proof just because they flow through this structure.
Use the harness to compare variants inside a controlled scenario, and avoid presenting those numbers as universal claims about model quality beyond that scenario.

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A minimal yet strict harness to run repeatable evaluations for RAG and agent pipelines. It fixes the two usual failures. First, non-reproducible runs. Second, noisy metrics that cannot explain drift. Everything here maps to WFGY pages with measurable targets.

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End to end retrieval knobs: Retrieval Playbook
• Why this snippet schema: Retrieval Traceability
• Payload schema and fences: Data Contracts
• Chunk quality before metrics: Chunking Checklist
• Similarity vs meaning: Embedding ≠ Semantic

Acceptance targets for this harness

• ΔS(question, retrieved) ≤ 0.45 on the gold set
• Coverage of the target section ≥ 0.70
• λ remains convergent across 3 paraphrases and 2 seeds
• Re-runs with identical seed produce metrics drift ≤ 0.5 percentage point

Folder layout and contracts

eval/
  datasets/
    gold/
      qa.jsonl            # minimal gold set
      citations.jsonl     # expected snippet anchors
    probes/
      paraphrases.jsonl   # 3 paraphrases per item
  runs/
    2025-08-29_seed42/
      config.yaml
      metrics.csv
      traces.jsonl
  config/
    harness.yaml          # store, retriever, reranker, seeds, k

Input schema

datasets/gold/qa.jsonl one JSON per line.

{
  "id": "Q_0001",
  "question": "How is vector contamination detected in FAISS indexes",
  "answer_ref": "PM:vectorstore-metrics-and-faiss-pitfalls#detect-contamination",
  "expected_doc": "ProblemMap/vectorstore-metrics-and-faiss-pitfalls.md",
  "section_id": "detect-contamination"
}

datasets/gold/citations.jsonl

{
  "id": "Q_0001",
  "snippet_id": "S_18823",
  "section_id": "detect-contamination",
  "source_url": "https://github.com/onestardao/WFGY/blob/main/ProblemMap/vectorstore-metrics-and-faiss-pitfalls.md",
  "offsets": [1380, 1540],
  "tokens": [310, 352]
}

Contract rules come from Retrieval Traceability and Data Contracts.

Repro knobs

• seed: integer. Set for the retriever, reranker, and LLM sampler if available.
• k: top k per retriever. Test 5, 10, 20.
• λ_observe: record λ state for retrieve, assemble, reason. See lambda_observe.md.
• ΔS probe: compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor). See deltaS_thresholds.md.

Execution flow

1. Warm up fence. Verify index hash, vector ready, secrets. If not ready, stop. Open: Bootstrap Ordering.

2. Retrieval step. Run with fixed metric and analyzer. Save raw hits with snippet fields from the contract page.

3. ΔS and λ probes. Log both per item. If ΔS ≥ 0.60 flag as structural risk.

4. Reasoning step. LLM reads TXT OS and uses the cite then explain schema. Refuse answers without citations.

5. Metrics. Compute precision, recall, citation hit, coverage. See eval_rag_precision_recall.md and Retrieval Playbook.

6. Trace sink. Write traces.jsonl with id, seed, k, ΔS, λ_state, snippet_id, section_id, INDEX_HASH.

7. Gate. If coverage < 0.70 or ΔS > 0.45 fail the run. See regression_gate.md.

Sixty second quick start

1. Place a ten item gold set into datasets/gold/qa.jsonl and citations.jsonl.
2. Copy config/harness.yaml from a previous good run. Set seed: 42, k: 10.
3. Run your script to produce runs/<date>_seed42/metrics.csv and traces.jsonl.
4. Verify the acceptance targets above. If any gate fails jump to the right fix below.

Common failures and the exact fix

• Wrong meaning despite high similarity. Open: Embedding ≠ Semantic

• Citations do not match the referenced section. Open: Retrieval Traceability and Data Contracts

• Hybrid retrieval worse than single retriever. Open: pattern_query_parsing_split.md and rerankers.md

• Runs flip across deployments or first run crashes. Open: deployment-deadlock.md, predeploy-collapse.md

• Long chains collapse. Open: context-drift.md and entropy-collapse.md

CI gates and artifacts

• Block merge if any of these is true

  1. ΔS median > 0.45 on gold
  2. Coverage < 0.70
  3. λ flips on 2 of 3 paraphrases
  4. Metrics drift from last green run > 0.5 percentage point

• Store artifacts metrics.csv, traces.jsonl, harness.yaml, INDEX_HASH, MODEL_HASH.

Copy paste prompts for the reasoning step

You have TXTOS and the WFGY Problem Map loaded.

Question: "{question}"
Retrieved snippets: [{snippet_id, section_id, source_url, offsets, tokens}]

Do:
1) Cite then explain. If citation is missing or mismatched, fail fast and return the minimal structural fix.
2) If ΔS(question, retrieved) ≥ 0.60 propose the smallest repair. Use retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3) Return JSON:
   {"citations":[...], "answer":"...", "λ_state":"→|←|<>|×", "ΔS":0.xx, "next_fix":"..."}
Keep it short and auditable.

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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

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 →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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