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English(EN) Harness-Delta Attribution: Dissecting Gains From Harness Evolution

新协议剖析AI代理收益，揭示过拟合问题

作者 PulseAugur 编辑部 · [1 个来源] ·

一种名为Harness-Delta Attribution (HDA) 的新评估协议已被引入，用于剖析代理系统中的性能提升。该方法将改进分为几类，例如对特定基准测试的过拟合、推理时间计算增加带来的收益以及残余的可泛化改进。当应用于11个执行器模型和三个Harness-evolution框架的各种任务时，HDA发现观察到的收益中有很大一部分是由于过拟合或测试时扩展，并且通常无法转移到未被测试的任务上。 AI

影响 强调了需要更强大的评估方法来防止AI代理开发中的过拟合。

排序理由 该集群包含一篇详细介绍AI代理系统新评估协议的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新协议剖析AI代理收益，揭示过拟合问题

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26 / 100
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Tool
该集群包含一篇详细介绍AI代理系统新评估协议的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wenxuan Ding, Qin Lu, Changlong Yu, Sha Li, Shuowei Jin, Xin Liu, Zhenyu Shi, Greg Durrett ·

    Harness-Delta Attribution: 解析Harness演进带来的收益

    arXiv:2610.22466v1 Announce Type: new Abstract: Harness evolution automatically optimizes the prompts, skills, tools, and orchestration code around a fixed-weight model, offering a way to improve agentic systems without updating model weights. The agent harness is iteratively opt…

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