# 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**.

---

## 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.

---

## 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. |

---

## 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.

---

## 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.  

---

## Example probe (pseudo)

```python
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"})
````

---

## 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.

---

## 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.

---

### 🔗 Quick-Start Downloads (60 sec)

| Tool                       | Link                                                                                                                                       | 3-Step Setup                                                                             |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| **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>”   |
| **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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### Explore More

| Layer | Page | What it’s for |
| --- | --- | --- |
| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |

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