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129 lines
7.3 KiB
Markdown
129 lines
7.3 KiB
Markdown
# Eval Observability — ΔS Thresholds
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A dedicated module for **ΔS monitoring** in evaluation pipelines.
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ΔS = semantic distance between query, retrieved content, and gold anchor.
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Tracking thresholds ensures that retrieval and reasoning quality remain **auditable, measurable, and comparable**.
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---
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## Why ΔS thresholds matter
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- **Detect semantic drift**: High ΔS despite “correct” tokens indicates meaning mismatch.
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- **Localize retrieval errors**: Low similarity in meaning even if vector scores look fine.
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- **Evaluate reasoning robustness**: Stable models keep ΔS below the risk boundary across paraphrases.
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- **Flag latent hallucinations**: ΔS >0.60 strongly correlates with unsupported answers.
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---
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## Core bands
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| Band | Range | Meaning |
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|------|-------|---------|
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| **Stable** | ΔS < 0.40 | Retrieval and reasoning aligned. Answers should be correct and verifiable. |
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| **Transitional** | 0.40 ≤ ΔS < 0.60 | Risk zone. Minor schema changes or index drift may flip outcomes. |
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| **Critical** | ΔS ≥ 0.60 | High failure probability. Almost always linked to missing context or schema break. |
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---
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## Acceptance targets
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- Per-query: **ΔS ≤ 0.45**
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- Batch average: **≤ 0.40**
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- Allowance: **≤ 10%** of queries can fall in the transitional band (0.40–0.60).
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- Critical: **0% tolerance** for ΔS ≥ 0.60 in gold-set eval.
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---
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## ΔS in eval workflow
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1. **Probe per query**
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Log ΔS(question, retrieved) and ΔS(retrieved, anchor).
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2. **Batch roll-up**
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Compute mean, variance, and percentile distribution.
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3. **Compare across seeds**
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Run three paraphrases and two random seeds; check convergence.
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4. **Drift alerting**
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If ΔS rises >0.05 vs baseline, trigger retraining or schema audit.
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---
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## Example probe (pseudo)
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```python
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def deltaS_probe(query, retrieved, anchor):
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d1 = deltaS(query, retrieved)
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d2 = deltaS(retrieved, anchor)
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return max(d1, d2)
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for q in eval_set:
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s = deltaS_probe(q.query, q.retrieved, q.anchor)
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if s >= 0.60:
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alerts.append({"qid": q.id, "ΔS": s, "status": "critical"})
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````
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---
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## Common pitfalls
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* **Using cosine similarity as ΔS** → ΔS is semantic distance, not raw vector score.
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* **Ignoring anchor comparison** → must compute against both query and gold span.
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* **No variance tracking** → averages hide volatility; variance is key.
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* **One-shot eval** → without paraphrase/seed checks, thresholds lack reliability.
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---
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## Reporting recommendations
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* **ΔS histogram**: visualize stability bands.
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* **Trend line**: track ΔS mean per batch over time.
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* **Baseline delta**: highlight drift from previous eval version.
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* **Failure clustering**: group queries where ΔS ≥0.60 for root-cause analysis.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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