Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring

• In-distribution, the graph model led an otherwise identical flat model by only 0.003 Avg@5 (p = 0.844 over six disjoint folds).
• RCAEval injects faults into only five services per system while exposing 12 to 70 in telemetry, so a ranker reading no telemetry puts the culprit in the top five on 99.7% of held-out incidents (Avg@5 0.488).
• The author argues that telemetry-free prior, not the uniform-random 0.137, is the honest in-distribution floor; it falls to 0.192 across systems.
• A non-uniform column schema silently zeroes telemetry for most RE1 cases, a second benchmark property that conditions any result on it.
• The proposed PSC-GRCA model reaches mean Avg@5 0.915 versus 0.864 for the flat baseline, but its ablations place most of the gain in the prior term, not the graph.

• benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.

Anyone building or evaluating RCA agents on RCAEval should know the graph component may not be earning its complexity, and that the benchmark's fault injection and scoring can mask this.

• articleKRCA: An Efficient Root Cause Analysis System in Hyper-Scale Microservice Systems via Agentic AIJiamin Jiang, Jingfei Feng, Yu Luo, Qingliang Zhang, Yongqian Su, Wenwei Gu, Shenglin Zhang, Tianyu Cui, Yao Wu, Jielong Huang, Nan Qi, Dan Pei
• articleRoot-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent FailuresHarsh Raj, David Lee, Anas Mahmoud, Renxiong Wang, Razvan-Gabriel Dumitru, Chenguang Wang, Tong Zhao, Yunzhong He, Darvin Yi, Vipul Gupta
• articleEviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause AnalysisYuhao Wang, Zhen Qin, Xingliang Wang, Guochang Li, Weize Li, Shuiguang Deng

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