Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

Identifying root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored systems. Existing studies evaluate root cause analysis (RCA) methods using top-k accuracy. We show that this metric has a fundamental blind spot: it conflates two failure modes: *retrieval failure*, where the true cause is never considered, and *reranking failure*, where it is considered but ranked too low. In this work, we introduce a retrieval-reranking decomposition and audit four well-known benchmarks to expose this blind spot. Our experiments show that, on benchmarks with complex faults, statistical baselines mis-rank the true cause 78-100\% of the time, and graph-based methods perform poorly because of unreliable causal graphs constructed using short fault windows. Meanwhile, on simple benchmarks where faults manifest significantly at their origin, retrieval is trivially solved at 100\%. Guided by the decomposition, we build a two-stage pipeline combining a multi-signal retriever with an LLM reranker that lifts top-1 accuracy by $+$2 to $+$26 points over the best baseline on complex-fault benchmarks and $+$9 to $+$13 points on simple benchmarks, with no causal graph or labeled data required.