IX2-0343
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Accuracy Evaluations - Line hint:
562
Context Before
Accuracy Evaluations
Throughput optimizations can sometimes quietly trade off accuracy (e.g. via aggressively relaxed acceptance rates, decoding tweaks, numerically unstable kernels, or endpoint misconfiguration). Without evals, a misconfigured server (truncation, bad decoding, wrong endpoint params) can still produce great throughput numbers but deliver garbage answers. For example, this additional layer of checks has helped us discover issues with some DP attention implementation for GPT-OSS.
Evidence
a harder benchmark may have a larger delta with respect to numerical accuracy
Context After
Another form of performance-accuracy tradeoff is quantization. Serving models at lower precision may result in worse model outputs. For DeepSeek R1, FP8 runs have very slightly higher evaluation scores than FP4. Note that GSM8k evals are saturated and often during QAT/PAT it is calibrated to common popular GSM8k, MATH-500, etc, leading to sometimes evals showing great results while real world end user evaluation being subpar. If we want to be part of the team to figure out how to properly evaluate inference engine accuracy, apply to join the mission here ↗.
② Atomic Claim
關於 InferenceX：更困難的 benchmark 在 numerical accuracy 上可能呈現更大的 delta。
- Epistemic Mode:
HYPOTHETICAL - Mapping Status:
PARTIAL
③ Semantic Frame
{
"attribute": "UNSPECIFIED_ATTRIBUTE",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/InferenceX",
"label": "InferenceX"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "關於 InferenceX：更困難的 benchmark 在 numerical accuracy 上可能呈現更大的 delta。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | InferenceX | InferenceX |
⑤ Human Review
請在 Properties 逐項確認：
- 原文 → Atomic Claim 是否忠實
- Atomic Claim → Semantic Frame 是否忠實
- Canonical Entity mapping 是否正確
- Epistemic mode 是否保留原文語氣
- 最後選擇
review_action
Review state
Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。