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ProblemMap/GlobalFixMap/Eval/eval_latency_vs_accuracy.md
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# Eval: Latency vs Accuracy Trade-off
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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.
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## Open these first
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* Core eval protocols: [Eval Benchmarking](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md)
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* Precision/recall metrics: [Eval RAG Precision/Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_rag_precision_recall.md)
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* 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)
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* Drift and variance: [variance\_and\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval_Observability/variance_and_drift.md)
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---
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## Acceptance targets
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* **Latency**:
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* Median ≤ 1.2× baseline
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* P90 ≤ 1.5× baseline
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* **Accuracy**:
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* Precision ≥ 0.80
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* Recall ≥ 0.70
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* ΔS(question, cited) ≤ 0.45 for ≥ 80 percent of runs
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* λ convergent across paraphrases
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* **Cost stability**:
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* Tokens or API cost per correct answer ≤ 1.3× baseline
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If accuracy improves but latency inflates beyond thresholds, classify as *not production-ready*. Only ship when both dimensions pass.
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---
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## Measurement protocol
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1. **Dual track runs**
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* Run with and without extra retrieval steps (rerank, multi-hop, HyDE, etc).
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* Record latency per stage (retrieve, rerank, reason).
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2. **Buckets**
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* Short queries: <50 tokens
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* Medium queries: 50–200 tokens
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* Long queries: >200 tokens
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Latency vs accuracy must be reported per bucket.
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3. **Seeds and paraphrases**
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* Use 2 random seeds, 3 paraphrases each.
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* Average and variance required for both latency and accuracy metrics.
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4. **Normalization**
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* Report cost per correct answer, not raw tokens.
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* Normalize across providers for fair comparison.
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---
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## Reporting schema
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Append to the JSONL logs from [Eval Benchmarking](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Eval/eval_benchmarking.md):
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```json
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{
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"suite": "v1_latency",
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"arm": "with_rerank",
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"provider": "openai",
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"model": "gpt-4o-mini-2025-07",
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"bucket": "medium",
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"precision": 0.82,
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"recall": 0.71,
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"ΔS_avg": 0.39,
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"λ_flip_rate": 0.02,
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"latency_ms": { "retrieve": 120, "rerank": 85, "reason": 910 },
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"latency_total_ms": 1115,
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"latency_vs_baseline": 1.35,
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"tokens": { "in": 1980, "out": 510 },
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"cost_per_correct": 1.25,
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"notes": "acceptable trade-off"
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}
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```
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---
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## Diagnostic questions
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When latency grows faster than accuracy:
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* Is reranking adding value or just delay? → check ΔS histograms pre/post rerank.
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* Are paraphrases redundant? → drop to 2 if λ stability holds.
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* Is retrieval k too large? → compare 5, 10, 20.
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* Are you re-embedding too often? → reuse cached vectors.
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* Is model size the bottleneck? → test smaller model + WFGY vs large model baseline.
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---
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## Escalation and fixes
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* **Latency regressions without accuracy gain** → cut rerank or hybrid steps. See [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md).
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* **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).
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* **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).
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---
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## Minimal 60-second run
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1. Pick 5 medium-length questions.
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2. Run baseline and WFGY rerank arm.
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3. Record latency\_total\_ms and accuracy metrics.
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4. Accept only if ΔS ≤ 0.45 and latency inflation ≤ 1.5× baseline.
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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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