IX2-0349
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Accuracy Evaluations - Line hint:
564
Context Before
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.
Each representative throughput config now has an associated numerical accuracy check. Currently we are only using GSM8k, but being a very easy benchmark, the evaluation scores may not change much from differences in numerical calculation, and a harder benchmark may have a larger delta with respect to numerical accuracy. Thus, we plan to expand towards harder ones in the future, such as GPQA, HLE, MATH-500, SWE-Bench verified.
Evidence
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
Context After
Source: SemiAnalysis InferenceX ↗
② Atomic Claim
原文此處邀請有興趣者加入團隊，共同研究如何正確評估 inference engine accuracy。
- Epistemic Mode:
ASSERTED - Mapping Status:
PARTIAL
③ Semantic Frame
{
"attribute": "UNSPECIFIED_ATTRIBUTE",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/Inference engine",
"label": "inference engine"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "原文此處邀請有興趣者加入團隊，共同研究如何正確評估 inference engine accuracy。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | inference engine | 04_knowledge_base/Inference engine |
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。