Eval Observability — Regression Gate

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A structural safeguard that enforces measurable thresholds before any pipeline is promoted to production.
Use this page to define hard acceptance criteria (ΔS, coverage, λ, resonance) and stop silent regressions from shipping.

Why regression gates matter

Catch semantic drift early: A small rise in ΔS leads to compounding hallucinations downstream.
Stable releases: Prevents model upgrades or retraining from silently reducing accuracy.
Auditable rules: Clear thresholds mean every team member can verify before deploy.
Cross-stack consistency: Same rules apply across providers, retrievers, and orchestration layers.

Core gate thresholds

Metric	Requirement	Failure signal
ΔS(question, retrieved)	≤ 0.45	drift ≥ 0.60 means block release
Coverage of target section	≥ 0.70	low coverage = missing context
λ_observe	Convergent across 3 paraphrases, 2 seeds	divergence = unstable reasoning
E_resonance	Flat on 50–100 step windows	spikes = entropy collapse risk

Deployment checklist

Pre-release batch eval
Run gold set of ~100–500 Q&A pairs. Collect ΔS, coverage, λ, resonance.
Gate decision
- If ΔS ≤ 0.45 AND coverage ≥ 0.70 → pass.
- If ΔS between 0.46–0.59 → manual review.
- If ΔS ≥ 0.60 OR coverage < 0.70 → fail, block release.
Variance probe
Check λ stability across 3 paraphrases × 2 seeds. Divergence disqualifies release.
Regression log
Store results with index hash + commit hash + retriever config.
Enables reproducibility and rollback.

Example gating script (pseudo)

# regression_gate.yml
metrics:
  deltaS: <=0.45
  coverage: >=0.70
  lambda: convergent
  resonance: flat
goldset: eval_set_500.json
policy:
  fail_on_drift: true
  manual_review_range: [0.46, 0.59]
  require_seeds: 2
  require_paraphrases: 3

Common pitfalls

Changing retriever k without updating gates. Always re-test thresholds.
Skipping paraphrase probes. One stable query is not enough.
Not logging coverage. ΔS alone cannot prove retrieval completeness.
Silent config drift. Gate must bind to exact retriever + index hash.

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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 →
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