开源工具包，专治LLM Agent故障，可观测、能归因、自动恢复，调试必备。
arXiv:2607.18754v1 Announce Type: new Abstract: LLM agent failures are difficult to debug because the step where an error surfaces is often not the on…
一篇来自arXiv的论文，系统评估大型语言模型在AI代理故障归因任务上的能力边界与不足。
arXiv:2607.09996v1 Announce Type: new Abstract: Automated failure attribution uses LLMs to identify where and why agentic systems fail. As agents beco…
智能体故障归因迎来新解法，主动调查机制突破上下文瓶颈，长轨迹诊断更精准高效。
arXiv:2606.24626v1 Announce Type: new Abstract: As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajec…
多智能体系统故障溯源难？基于时间语义的新框架StepFinder，被KDD 2026接收，帮系统快速定位协作失败根源。
arXiv:2606.03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step task…
提出假设验证方法定位LLM多智能体系统故障原因，提升系统可靠性
arXiv:2605.17467v1 Announce Type: new Abstract: Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agent…