WFGY/ProblemMap/GlobalFixMap/Eval_Observability/deltaS_thresholds.md

Eval Observability — ΔS Thresholds

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A dedicated module for ΔS monitoring in evaluation pipelines.
ΔS = semantic distance between query, retrieved content, and gold anchor.
Tracking thresholds ensures that retrieval and reasoning quality remain auditable, measurable, and comparable.

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Why ΔS thresholds matter

• Detect semantic drift: High ΔS despite “correct” tokens indicates meaning mismatch.
• Localize retrieval errors: Low similarity in meaning even if vector scores look fine.
• Evaluate reasoning robustness: Stable models keep ΔS below the risk boundary across paraphrases.
• Flag latent hallucinations: ΔS >0.60 strongly correlates with unsupported answers.

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Core bands

Band	Range	Meaning
Stable	ΔS < 0.40	Retrieval and reasoning aligned. Answers should be correct and verifiable.
Transitional	0.40 ≤ ΔS < 0.60	Risk zone. Minor schema changes or index drift may flip outcomes.
Critical	ΔS ≥ 0.60	High failure probability. Almost always linked to missing context or schema break.

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Acceptance targets

• Per-query: ΔS ≤ 0.45
• Batch average: ≤ 0.40
• Allowance: ≤ 10% of queries can fall in the transitional band (0.40–0.60).
• Critical: 0% tolerance for ΔS ≥ 0.60 in gold-set eval.

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ΔS in eval workflow

1. Probe per query
   Log ΔS(question, retrieved) and ΔS(retrieved, anchor).
2. Batch roll-up
   Compute mean, variance, and percentile distribution.
3. Compare across seeds
   Run three paraphrases and two random seeds; check convergence.
4. Drift alerting
   If ΔS rises >0.05 vs baseline, trigger retraining or schema audit.

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Example probe (pseudo)

def deltaS_probe(query, retrieved, anchor):
    d1 = deltaS(query, retrieved)
    d2 = deltaS(retrieved, anchor)
    return max(d1, d2)

for q in eval_set:
    s = deltaS_probe(q.query, q.retrieved, q.anchor)
    if s >= 0.60:
        alerts.append({"qid": q.id, "ΔS": s, "status": "critical"})

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Common pitfalls

• Using cosine similarity as ΔS → ΔS is semantic distance, not raw vector score.
• Ignoring anchor comparison → must compute against both query and gold span.
• No variance tracking → averages hide volatility; variance is key.
• One-shot eval → without paraphrase/seed checks, thresholds lack reliability.

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Reporting recommendations

• ΔS histogram: visualize stability bands.
• Trend line: track ΔS mean per batch over time.
• Baseline delta: highlight drift from previous eval version.
• Failure clustering: group queries where ΔS ≥0.60 for root-cause analysis.

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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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Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
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Map	Semantic Clinic	Symptom to family to exact fix
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