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English(EN) Where Should a Stuck Agent Spend Its Next Tokens? Causal Attribution of Inference-Time Resources

新框架揭示AI代理偏爱无效资源

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的研究论文介绍了一种名为Banzhaf资源归因（BRA）的框架，该框架旨在衡量不同资源对修复失败AI代理的边际贡献。研究评估了六个模型在编码任务上的表现，发现与任务相关的规范能显著改善修复效果，而公共测试反馈带来的收益很小，检索到的代码几乎没有益处。研究还指出，前沿模型常常优先考虑反馈而非更有效的资源，这表明代理偏好与实际修复效果之间存在差距。 AI

影响 强调了AI代理如何利用资源方面的一个关键差距，表明需要改进代理偏好与有效问题解决策略之间的一致性。

排序理由 学术论文，详细介绍了一个新框架和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新框架揭示AI代理偏爱无效资源

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学术论文，详细介绍了一个新框架和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Saiyue Lyu, Jerrold Huang, Yunbei Zhang, Janet Wang, Zhenting Wang, Yingqiang Ge, Haibo Zhang ·

    Where Should a Stuck Agent Spend Its Next Tokens? Causal Attribution of Inference-Time Resources

    arXiv:2610.28834v1 Announce Type: cross Abstract: Inference-time scaling is often treated as a token-budget problem, but a failed coding agent can seek qualitatively different resources: computation, information, or feedback. We introduce Banzhaf Resource Attribution (BRA), a con…

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