Root Cause Analysis

Also known as RCA

Root cause analysis asks which underlying fault started a failure, rather than merely identifying the components or signals that look abnormal. It matters in settings such as microservices, networks, industrial systems, and software operations, where faults propagate and symptoms can appear far from their source. Methods range from statistical change detection and causal graph discovery to counterfactual testing, multimodal telemetry analysis, and language-model agents that retrieve evidence and check hypotheses. A central challenge is distinguishing causal evidence from correlation: benchmark scores can hide weak evidence retrieval or unverified reasoning, and agents remain unreliable when telemetry is incomplete, misleading, or spread across long incident histories.

Latest papers

81 papers8 papers in the last four weeks, down 11% on the four weeks before. 0.0% of all new papers.

Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
Jul 29, 2026Seunghun Yu, Meiyi Zhu, Petar Popovski +2Conformal Prediction Change-Point Detection
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea · Department of Engineering, King’s College London, WC2R 2LS, London, U.K. · Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark +2
Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks
Jul 28, 2026Md. Kamrul Hossain, Walid AljobyRoot Cause Analysis Intent-Based Networking
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Information and Computer Science Department, and IRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Agentic Root Cause Analysis through Evidence-Grounded Reasoning
Jul 24, 2026Amaury Wei, Olga FinkIndustrial Anomaly Detection Agentic Reasoning
EPFL - IMOS Laboratory CH-1015 Lausanne Switzerland
How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
Jul 15, 2026Athira Gopal, Ashwanth KrishnanAgent Failure Analysis Root Cause Analysis
QPIAI India
Actual causality in fault trees
Jul 2, 2026Georgiana Caltais, Milan Lopuhaä-Zwakenberg, Mariëlle StoelingaActual Causality Causal Attribution
University of Twente, The Netherlands · Radboud University, The Netherlands
A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis
Jun 28, 2026Yuanhong Cai, Xiaohui Nie, Kanglin Yin +8Software Engineering LLM Agent Evaluation
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Key Laboratory for Satellite Digitalization Technology, Chinese Academy of Sciences, Shanghai, China · Nankai University, Tianjin, China +2
What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarks
Jun 28, 2026Lining Hu, Ting Liu, Yuzhuo FuPairwise Comparison Algorithmic Auditing
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage
Jun 25, 2026Ali Semih Atalay, Sevgi Yigit-SertMulti-Task Learning Root Cause Analysis
aDepartment of Computer Engineering, Faculty of Engineering, Ankara University, Ankara, 06830, Türkiye
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Jun 25, 2026Aoyang Fang, Yifan Yang, Jin'ao Shang +7LLM Agent Evaluation Causal Attribution
The Chinese University of Hong Kong, Shenzhen · Lero · The Chinese University of Hong Kong
Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs
Jun 18, 2026Henrique O. Caetano, Rafael Arone, Carlos Dias MacielCausal Inference with Unobserved Confounding Partial Identification
Signal Processing Laboratory, São Carlos School of Engineering, University of São Paulo, São Paulo, Brazil · Guaratinguetá School of Engineering, São Paulo State University, Guaratinguetá, Brazil
A Topology-Aware, Memory-Centric Architecture that Separates Root-Cause Derivation from Root-Cause Explanation
Jun 18, 2026Momil SeedatSoftware Engineering Microservice Architecture
Independent Research Prototype
JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis
Jun 17, 2026Tingzhu Bi, Xinrui Jiang, Xun Zhang +5AI Accountability Evidence-Grounded Reasoning
Peking University · University of Edinburgh · Beijing University of Posts and Telecommunications
SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles
Jun 14, 2026Matthias Weiß, Athreya Hosahalli Prakash, Falk Dettinger +2Causal Discovery Root Cause Analysis
Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart Pfaffenwaldring 47, 70550 Stuttgart, Germany
NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
Jun 11, 2026Fabien Chraim, Jian Zhang, Dominik Janzing +3Anomaly Localization Causal Inference
Amazon Web Services, Seattle, Washington, USA
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
Jun 11, 2026Fabien Chraim, Dominik Janzing, John EvansTime-Series Causal Discovery Cloud Computing
Amazon Web Services
Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis
Jun 11, 2026Seongjin Kim, Sungil KimRetrieval-Augmented Generation Hybrid Retrieval
Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.
Anomaly Detection and Root Cause Analysis for Microservice Systems
Jun 8, 2026Luan PhamSoftware Engineering Microservice Architecture
Viet Nam National University Ho Chi Minh City - University of Technology (HCMUT) · School of Computing Technologies, College of Science, Technology, Engineering and Maths, Royal Melbourne Institute of Technology (RMIT University), Melbourne, Victoria, Australia
Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
Jun 7, 2026Anastasiia Kuvshinova, Seungmin JinLLM Auditing Graph Reasoning
HSE University Moscow, Russia
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Jun 4, 2026Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2Root Cause Analysis Causal Intervention
Computer Science Department TU Darmstadt, Germany
Ekka: Automated Diagnosis of Silent Errors in LLM Inference
Jun 3, 2026Yile Gu, Zhen Zhang, Shaowei Zhu +4Software Engineering LLM Serving
University of Washington · 2Amazon Web Services.
PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis
May 30, 2026Zongzong Wu, Ming Zhao, Fengxiao Tang +1Root Cause Analysis LLM Reasoning
School of Computer Science and Engineering, Central South University, Changsha, China · Graduate School of Information Sciences (GSIS), Tohoku University, Sendai, Japan
Formalizing and falsifying causal pathways of rare events
May 29, 2026Anahita Haghighat, Dominik JanzingStructural Causal Models Causal Abstraction
Independent · Amazon Research, Tübingen, Germany
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
May 26, 2026Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj +2AI-Assisted Decision Making Causal Discovery
Bosch Global Software Technologies Company Limited, Vietnam · Robert Bosch GmbH, Germany · Computer Science Department, TU Darmstadt, Germany +2
Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining
May 26, 2026Biao Ouyang, Tengxue Zhang, Zhihao Zhuang +3Time-Series Causal Discovery OOD Generalization
East China Normal University
LogDx-CI: Benchmarking Log Reduction Tools for LLM Root-Cause Diagnosis
May 26, 2026Bowen QinLLM Agent Evaluation AI Agent Benchmarks
National University of Singapore
Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows
May 18, 2026Dhairya Dalal, Endre Sara, Ben Yemini +2Software Engineering AI Agent Benchmarks
Causely
Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports
May 17, 2026Mahmut Furkan Gon, Emre Dinc, Tevfik Emre Sungur +1Customer Support Automation Root Cause Analysis
Department of Computer Engineering, Bilkent University, Ankara, T̈urkiye.
TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
May 15, 2026Junle Wang, Xingchuang Liao, Wenjun WuSoftware Engineering Multi-Agent Orchestration
School of Artificial Intelligence, Beihang University Beijing, China
STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices
May 15, 2026Junle Wang, Xingchuang Liao, Wenjun WuAgent Failure Analysis Root Cause Analysis
School of Artificial Intelligence, Beihang University
Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
May 14, 2026Lingzhe Zhang, Tong Jia, Kangjin Wang +8Software Engineering Multi-Agent LLM Systems
Peking University, Beijing, China · Huawei Theory Lab, China

Jul 29, 2026J/K move · Enter open · S save

Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations

Seunghun Yu, Meiyi Zhu, Petar Popovski, Joonhyuk Kang+1

Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea · Department of Engineering, King’s College London, WC2R 2LS, London, U.K. · Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark+2

Conformal Prediction Change-Point Detection Data Contamination Root Cause Analysis

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Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication networks, robotic platforms, security infrastructure, and multi-agent systems. In safety- and mission-critical deployments, such decisions must be accompanied by statistical reliability guarantees rather than by point estimates alone. Conformal changepoint localization (CONCH) and conformal root cause analysis (CROC) meet this need by returning confidence sets that contain the true changepoint, or the true root-cause stream, with a user-specified probability, without parametric assumptions on the data-generating process. In practice, however, observations are frequently corrupted, e.g., by outliers, sensor faults, or adversarial perturbations. While the finite-sample coverage of these procedures is preserved under contamination, the resulting confidence sets can become uninformatively large. Adopting a Huber-type contamination model, this paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC), which downweight observations that are likely to be corrupted with the goal of reducing confidence set size when data may be corrupted. The weighting mechanism, derived from a formal bound on the unknown corrupted data densities, leverages pre-existing second-order classifier-based uncertainty signals, such as those produced by evidential deep learning or Bayesian learning. W-CONCH and W-CROC are further generalized by introducing a meta-learning procedure for the weights that optimizes a differentiable surrogate of the confidence set size. Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size while maintaining the target coverage.