# Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

arXiv: https://arxiv.org/abs/2609.36686 · HTML: https://mlsanity.com/paper/2609.36686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 50 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 30 of 36)

- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Zero-Shot Listwise Document Reranking with a Large Language Model](https://mlsanity.com/paper/2305.02156/) (2023)
- [Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning](https://mlsanity.com/paper/2503.06034/) (2025)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [FIRST: Faster Improved Listwise Reranking with Single Token Decoding](https://mlsanity.com/paper/2406.15657/) (2024)
- [MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems](https://mlsanity.com/paper/2103.01782/) (2021)
- [Root Cause Analysis In Microservice Using Neural Granger Causal Discovery](https://mlsanity.com/paper/2402.01140/) (2024)
- [RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data](https://mlsanity.com/paper/2412.17015/) (2024)
- [Rank-K: Test-Time Reasoning for Listwise Reranking](https://mlsanity.com/paper/2505.14432/) (2025)
- [D-Bot: Database Diagnosis System using Large Language Models](https://mlsanity.com/paper/2312.01454/) (2023)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection](https://mlsanity.com/paper/2405.09330/) (2024)
- [Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning](https://mlsanity.com/paper/1709.05342/) (2017)
- [Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization](https://mlsanity.com/paper/2302.01987/) (2023)
- [Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?](https://mlsanity.com/paper/2408.13729/) (2024)
- [The PetShop Dataset -- Finding Causes of Performance Issues across Microservices](https://mlsanity.com/paper/2311.04806/) (2023)
- [Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models](https://mlsanity.com/paper/2312.02969/) (2023)
- [Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2508.20370/) (2025)
- [Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight](https://mlsanity.com/paper/2407.08694/) (2024)
- [Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection](https://mlsanity.com/paper/2008.12618/) (2020)
- [Root cause discovery via permutations and Cholesky decomposition](https://mlsanity.com/paper/2410.12151/) (2024)
- [LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis](https://mlsanity.com/paper/2406.05375/) (2024)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking](https://mlsanity.com/paper/2505.18512/) (2025)
- [Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism](https://mlsanity.com/paper/2601.02736/) (2026)
- [TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems](https://mlsanity.com/paper/2310.18740/) (2023)
- [Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization](https://mlsanity.com/paper/1807.07282/) (2018)
- [Graph-Free Root Cause Analysis](https://mlsanity.com/paper/2601.21359/) (2026)
- [TORAI: Multi-source Root Cause Analysis for Blind Spots in Microservice Service Call Graph](https://mlsanity.com/paper/2604.13522/) (2026)
- [FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications](https://mlsanity.com/paper/2412.02239/) (2024)

## Abstract (arXiv)

Identifying the 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 79-100% of the time, and graph-based methods never clearly beat the best statistical baseline, whether their causal graphs are learned on short fault windows, on retrieved candidate pools guaranteed to contain the cause, or on multi-day normal-operation data. Meanwhile, on simple benchmarks where faults manifest significantly at their origin, retrieval is nearly solved (98-100%). Guided by the decomposition, we build a two-stage pipeline combining a multi-signal retriever with an LLM reranker that, as one fixed configuration, matches or exceeds the best baseline's top@1 accuracy on all six benchmark suites (by up to +12 points), with no causal graph or labeled data required. When all methods rank the same retrieved candidates with the true cause guaranteed present, adding a short system-description document lets the reranker lead the best baseline by +7 to +18 points on every benchmark. Code is available at https://github.com/cruiseresearchgroup/DecompRCA.