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English(EN) Your Harness Didn’t Get Better, It Got Lucky: Noise Bands for Agent Evals

AI Agent 评估需要噪声带来区分真正进展和运气

最近的一项分析强调了评估 AI Agent 时“噪声带”的关键问题,表明许多报告的分数提高可能源于运气而非真正的进展。作者使用了 Paolo Perrone 关于 Terminal-Bench 2.0 的数据来说明评估 Harness 的变化如何导致误导性的分数波动。例如,一次 Harness 更换导致了噪声带内的小分数变化,而一次模型更换则显示出明显的下降,这明显超出了噪声带,表明存在真实的性能差异。 AI

影响 强调了需要稳健的评估方法来准确评估 AI Agent 的性能,并避免将随机波动误解为真正的改进。

排序理由 该项目讨论了评估 AI Agent 的一种方法,引用了已发布的数据和分析,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 Towards AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI Agent 评估需要噪声带来区分真正进展和运气

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该项目讨论了评估 AI Agent 的一种方法,引用了已发布的数据和分析,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
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High
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Breaking (< 6h)
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完整方法见我们的编辑标准。

报道来源 [1]

  1. Towards AI TIER_1 English(EN) · Ali Süleyman TOPUZ ·

    你的束缚并未变好,只是侥幸:代理评估的噪声带

    <h4>A 2.5-point drop, a 20.4-point drop, and the one question that tells them apart</h4><p>Picture a pull request that changes the harness around your coding agent: a new system prompt, a different tool layout, maybe a whole new CLI wrapped around the same model. The description …