# Eval: Latency vs Accuracy Trade-off
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> - [**Eval** — model evaluation and benchmarking](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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> **Evaluation disclaimer (latency vs accuracy)**
> The trade off curves and numbers here depend on your stack, load and datasets.
> Treat them as shapes to look for, not fixed targets that prove one model or setting is always better.
---
This page defines how to measure, report, and optimize the trade-off between model latency and retrieval/answer accuracy. It is not enough to chase precision; stable systems must also meet latency SLOs while holding ΔS and λ within guardrails.
## Open these first
* Core eval protocols: [Eval Benchmarking](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md)
* Precision/recall metrics: [Eval RAG Precision/Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_rag_precision_recall.md)
* Observability instruments: [deltaS\_thresholds.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/deltaS_thresholds.md), [lambda\_observe.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/lambda_observe.md)
* Drift and variance: [variance\_and\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/variance_and_drift.md)
---
## Acceptance targets
* **Latency**:
* Median ≤ 1.2× baseline
* P90 ≤ 1.5× baseline
* **Accuracy**:
* Precision ≥ 0.80
* Recall ≥ 0.70
* ΔS(question, cited) ≤ 0.45 for ≥ 80 percent of runs
* λ convergent across paraphrases
* **Cost stability**:
* Tokens or API cost per correct answer ≤ 1.3× baseline
If accuracy improves but latency inflates beyond thresholds, classify as *not production-ready*. Only ship when both dimensions pass.
---
## Measurement protocol
1. **Dual track runs**
* Run with and without extra retrieval steps (rerank, multi-hop, HyDE, etc).
* Record latency per stage (retrieve, rerank, reason).
2. **Buckets**
* Short queries: <50 tokens
* Medium queries: 50–200 tokens
* Long queries: >200 tokens
Latency vs accuracy must be reported per bucket.
3. **Seeds and paraphrases**
* Use 2 random seeds, 3 paraphrases each.
* Average and variance required for both latency and accuracy metrics.
4. **Normalization**
* Report cost per correct answer, not raw tokens.
* Normalize across providers for fair comparison.
---
## Reporting schema
Append to the JSONL logs from [Eval Benchmarking](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md):
```json
{
"suite": "v1_latency",
"arm": "with_rerank",
"provider": "openai",
"model": "gpt-4o-mini-2025-07",
"bucket": "medium",
"precision": 0.82,
"recall": 0.71,
"ΔS_avg": 0.39,
"λ_flip_rate": 0.02,
"latency_ms": { "retrieve": 120, "rerank": 85, "reason": 910 },
"latency_total_ms": 1115,
"latency_vs_baseline": 1.35,
"tokens": { "in": 1980, "out": 510 },
"cost_per_correct": 1.25,
"notes": "acceptable trade-off"
}
```
---
## Diagnostic questions
When latency grows faster than accuracy:
* Is reranking adding value or just delay? → check ΔS histograms pre/post rerank.
* Are paraphrases redundant? → drop to 2 if λ stability holds.
* Is retrieval k too large? → compare 5, 10, 20.
* Are you re-embedding too often? → reuse cached vectors.
* Is model size the bottleneck? → test smaller model + WFGY vs large model baseline.
---
## Escalation and fixes
* **Latency regressions without accuracy gain** → cut rerank or hybrid steps. See [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md).
* **High ΔS despite more steps** → rebuild index and re-chunk. See [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md).
* **Unstable λ across seeds** → clamp variance with BBAM, see [variance\_and\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/variance_and_drift.md).
---
## Minimal 60-second run
1. Pick 5 medium-length questions.
2. Run baseline and WFGY rerank arm.
3. Record latency\_total\_ms and accuracy metrics.
4. Accept only if ΔS ≤ 0.45 and latency inflation ≤ 1.5× baseline.
---
### 🔗 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 + \” |
| **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 |
---
### 🧭 Explore More
| Module | Description | Link |
| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| 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) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| 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) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| 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) |
| 🧙♂️ 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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