---
id: 20260917-T0-07
title: "长周期AI智能体失败诊断新思路：把根因归因当作搜索问题"
title_en: "Root-Cause Attribution for Long-Horizon Agent Failures as a Search Problem"
url: https://ai.daily.yangsir.net/daily/20260917-T0-07
issue_date: 2026-09-17
publish_date: 2026-09-16T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2609.13463
---

# 长周期AI智能体失败诊断新思路：把根因归因当作搜索问题

新论文提出将长周期AI智能体失败的根因归因视为一个搜索问题，通过持续搜索从海量执行日志中定位失败原因。随着AI智能体在长周期任务中部署增加，执行日志规模急剧膨胀，如何从记录中诊断失败对系统可靠性至关重要。该方法将结果层面的信号转化为可操作的干预措施。这意味着运维团队未来可以更高效地定位智能体在复杂任务中的失败节点，减少人工排查日志的时间成本。

## English Version

**Root-Cause Attribution for Long-Horizon Agent Failures as a Search Problem**

A new paper frames root-cause attribution for long-horizon AI agent failures as a search problem, using continual search to locate failure causes within massive execution logs. As AI agents are increasingly deployed in long-horizon tasks, execution logs explode in scale, making failure diagnosis critical for reliability. The method transforms outcome-level signals into actionable interventions. This means operations teams could more efficiently pinpoint failure points in complex agent tasks, reducing the manual time spent sifting through logs.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2609.13463)

**详情页**：https://ai.daily.yangsir.net/daily/20260917-T0-07

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