arXiv:2609.13463 · Agent 智能体
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
根因归因是一个搜索问题：面向长时序 Agent 失败的持续搜索
Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
- 类型：arxiv
- 标识：2609.13463
- 链接：https://arxiv.org/abs/2609.13463
- 主分类：agent
- 形态：application
- TLDR：The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the need for automated root-cause attribution (RCA). However, automated RCA methods using LLMs suffer from low diagnostic accuracy, especially as execution traces grow larger. They struggle because relevant information is often sparse, distributed across distant actions, and disconnected from the v
- 待LLM分类：否
- 标题中文：根因归因是一个搜索问题：面向长时序 Agent 失败的持续搜索
- TLDR中文：AI Agent 在长时序任务中日益广泛的应用产生了海量执行日志。对这些记录中的失败进行诊断对可靠性至关重要，因为它能将结果层面的信号转化为可操作的干预。数据的庞大规模使人工审查不切实际，催生了对自动化根因归因（RCA）的需求。然而，基于 LLM 的自动化 RCA 方法诊断准确率较低，尤其当执行轨迹增大时更为明显。其表现欠佳的原因在于：相关信息往往稀疏、分散于相距甚远的动作之间，并与导致失败的决策脱节...
- 来源文件：
- /inbox/tom/_candidates/2026-09-16-agent-rag-longcontext-candidates.json