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

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

Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning
May 11, 2026Yuna Haseyama, Tomoki Ito, Hiroki Sakaji +1Healthcare Few-Shot Learning
Graduate School of Information Science and Technology Hokkaido University Hokkaido, Japan · National Institute of Information and Communications Technology (NICT) Kyoto, Japan · Faculty of Information Science and Technology Hokkaido University Hokkaido, Japan
PRIM: Meta-Learned Bayesian Root Cause Analysis
May 9, 2026Christopher Lohse, Anish Dhir, Amadou Ba +3Meta-Learning Neural Processes
University of Dublin, Trinity College, Dublin, Ireland · IBM · Gatsby Computational Neuroscience Unit, University College London +2
SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting
May 6, 2026Kuan-Hao Tseng, Niruth Bogahawatta, Yasod Ginige +3LLM Agent Orchestration LLM Agents
University of Sydney, Sydney, Australia · University of New South Wales, Sydney, Australia
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
May 1, 2026Luan Pham, Victor Nicolet, Joey Dodds +2Online Anomaly Detection Anomaly Localization
RMIT University, Australia · Amazon Web Services, United States
Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
May 1, 2026Xin Liu, Yuhang He, Sichen Zhao +2Microservice Architecture Root Cause Analysis
Independent Researcher New York, USA · Independent Researcher Chicago, USA · Northeastern University Boston, USA +2
Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
Apr 29, 2026Bochao Liu, Zhipeng Qian, Yang Zhao +10Continual Learning for LLM Agents LLM Agent Orchestration
Kuaishou Technology
Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics
Apr 28, 2026Pernilla Hall, Anton Ununger, Riccardo Rubei +1Software Engineering Root Cause Analysis
ABB Robotics · Sweden · Mälardalen University
Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection
Apr 19, 2026Shashank Mishra, Karan Patil, Cedric Schockaert +2Feature Attribution Interpretable Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Germany · Paul Wurth S.A, Luxembourg
OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning
Apr 6, 2026Jingkai He, Pengfei Chen, Chenghui Wu +7LLM Post-Training LLM Fine-Tuning
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China · School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Alibaba Cloud Computing, Hangzhou, China
Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry
Mar 31, 2026Syed Eqbal Alam, Zhan ShuMulti-Agent Collaboration Multimodal Agents
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada · SheQAI Research, Edmonton, Alberta, Canada
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
Mar 31, 2026Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson +1Smart Manufacturing Root Cause Analysis
Artificial Intelligence Institute, University of South Carolina · Stride Lab, University of Michigan, Dearborn · Bosch Center for Artificial Intelligence, Pittsburgh
ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality
Mar 26, 2026Wenzhuo Qian, Hailiang Zhao, Ziqi Wang +4Root Cause Analysis Multimodal Anomaly Detection
Zhejiang University, Hangzhou, China
Praxium: Diagnosing Cloud Anomalies with AI-based Telemetry and Dependency Analysis
Mar 25, 2026Rohan Kumar, Jason Li, Zongshun Zhang +3Anomaly Detection Root Cause Analysis
Boston University
AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks
Mar 24, 2026Md. Kamrul Hossain, Walid AljobyIntent-Based Networking Language Model-Based Control
Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · IRC for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
Mar 24, 2026Yuhui Wang, Zhixiong Yang, Ming Zhang +10LLM Evaluation Multi-Turn Dialogue Evaluation
1Fudan University
NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum
Mar 22, 2026Peihan Ye, Alfreds Lapkovskis, Alaa Saleh +2Root Cause Analysis Edge-Cloud Computing
Department of Computer Systems and Sciences, Stockholm University, Stockholm 164 25, Sweden · Center for Applied Computing, University of Oulu, Oulu 90014, Finland · Computer Science School, Peking University, Beijing 100876, China
Automating Detection and Root-Cause Analysis of Flaky Tests in Quantum Software
Mar 9, 2026Janakan Sivaloganathan, Ainaz Jamshidi, Andriy Miranskyy +1Root Cause Analysis Automated Software Testing
Department of Computer Science, Toronto Metropolitan University, Toronto, Canada · Department of Information Systems, University of Maryland, Baltimore County, Baltimore County, United States of America
Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems
Feb 25, 2026Ayush Mohanty, Paritosh Ramanan, Nagi GebraeelRoot Cause Analysis Heterogeneous Federated Learning
School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 · School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74708
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
Feb 13, 2026Ayse Tursucular, Ayush Mohanty, Nazal Mohamed +1Federated Learning Nonlinear System Identification
Georgia Institute of Technology Atlanta, GA, USA
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
Oct 8, 2025Daniela Schkoda, Dominik JanzingStructural Causal Models Root Cause Analysis
Amazon Research Tübingen, Germany · Technical University of Munich, Germany

May 11, 2026J/K move · Enter open · S save

Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning

Yuna Haseyama, Tomoki Ito, Hiroki Sakaji, Itsuki Noda

Graduate School of Information Science and Technology Hokkaido University Hokkaido, Japan · National Institute of Information and Communications Technology (NICT) Kyoto, Japan · Faculty of Information Science and Technology Hokkaido University Hokkaido, Japan

Healthcare Few-Shot Learning Root Cause Analysis Medical LLMs

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In high-stakes domains such as healthcare, the reliability of Large Language Models (LLMs) is critical, particularly when generating clinical insights from incident reports. This study proposes a tag-based few-shot example selection method for prompting LLMs to generate background/causal factors and preventive measures from details of the medical incidents. For our experiments, we use the Japanese Medical Incident Dataset (JMID), a structured dataset of 3,884 real-world medical accident and near-miss reports. These reports are variably annotated with a wide range of tags--some include descriptive information (e.g., "medications," "blood transfusion therapy"). We compare three few-shot example selection strategies--random sampling, cosine similarity-based selection, and our proposed tag-based method--using GPT-4o and LLaMA 3.3. Results show that the tag-based approach achieves the highest precision and most stable generation behavior, while similarity-based selection often leads to unintended outputs and safety filter activation. These findings suggest that selecting examples based on human-interpretable dataset tags can improve generation precision and stability in clinical LLM applications.