拓扑感知多智能体框架自进化解决微服务根因分析，应对噪声、级联故障和拓扑漂移三大挑战
arXiv:2605.15611v1 Announce Type: new Abstract: Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimoda…
用元学习+贝叶斯推断搞定复杂系统根因分析，无需完整因果图即可高效定位故障。
arXiv:2605.08786v2 Announce Type: replace Abstract: Root cause analysis (RCA) in complex systems is challenging due to error propagation across multip…
LLM-based RCA代理易出错？STAR框架通过阶段分诊与修复提升微服务故障诊断可靠性。
arXiv:2605.15581v1 Announce Type: new Abstract: LLM-based root cause analysis (RCA) agents have recently emerged as a promising paradigm for incident …
用超图本体打破LLM在企业系统中的幻觉与审计困境，HEAR在供应链根因分析上实现94.7%准确率，为多跳、n元推理提供了无需重训的可扩展范式。
arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucination…