{
  "id": 4285,
  "url": "https://arxiv.org/abs/2605.15611v1",
  "title": "TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices",
  "summary": "Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (iii) non-stationary topology drift induced by autoscaling and rolling updates. Recent LLM-based RCA agents can generate tool-grounded explanations, yet they often remain topology-agnostic and suffer from \\emph{symptom-amplification bias}, misattributing the root cause to salient d",
  "authors": "Junle Wang, Xingchuang Liao, Wenjun Wu",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-15T04:45:44.000Z",
  "fetched_at": "2026-07-14T16:30:54.919Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4285",
  "original_url": "https://arxiv.org/abs/2605.15611v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}