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7.2 KiB
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Eval Observability — Variance and Drift
Variance and drift checks detect when evaluation scores are unstable across runs or when semantic meaning slowly shifts without clear boundary failures.
These probes prevent "false confidence" in benchmarks by catching hidden instability.
Why variance and drift matter
- Variance: Scores fluctuate heavily depending on seed, paraphrase, or retriever order. Averages hide the volatility.
- Drift: Performance declines slowly across sessions, data refreshes, or version bumps. Looks fine short-term but collapses long-term.
- Silent regressions: Systems pass local tests but fail in production due to unmonitored entropy rise.
Acceptance targets
- Variance (σ/μ) ≤ 0.15 across 3 seeds and 3 paraphrases.
- Drift slope: Δscore per batch ≤ 0.02 absolute over 5+ eval windows.
- No monotonic downward slope longer than 3 consecutive windows.
- Drift alerts fire if ΔS average increases ≥ 0.10 compared to gold anchors.
Detection workflow
-
Collect runs across seeds
- At least 3 seeds, 3 paraphrases.
- Log ΔS, λ, coverage, citations.
-
Compute variance
- Calculate σ/μ for each metric.
- High variance = unstable eval → rerun with schema locks.
-
Track drift over time
- Compare eval batch N vs N-1.
- Plot moving average.
- Alert if slope exceeds tolerance.
-
Root-cause analysis
- If variance high → check retriever metrics, random seeding, rerankers.
- If drift detected → audit embeddings, re-chunk, verify data refresh.
Common pitfalls
- Single-run evals: Hides high variance. Always run multi-seed.
- Averages without spread: Mean looks fine, variance reveals collapse.
- Ignoring slow drift: Short tests OK, but 1–2 weeks later accuracy dies.
- Cross-store drift: One vector DB stable, another drifts. Must track both.
Example reporting schema
{
"metric": "ΔS",
"seed_runs": [0.38, 0.42, 0.44],
"variance_ratio": 0.14,
"drift_slope": +0.03,
"alert": true
}
Fix modules to open
- Retriever instability → Rerankers
- Embedding mismatch → embedding-vs-semantic.md
- Fragmentation drift → vectorstore-fragmentation.md
- Prompt instability → context-drift.md
🔗 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
| 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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