Anomaly Localization

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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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
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision
Jun 8, 2026Mateo Diaz-Bone, Daniel Caraballo, Florian Scheidegger +11Anomaly Localization Reconstruction-Based Anomaly Detection
IBM Research Europe Zurich, Switzerland
Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks
Jun 1, 2026Weibai Fang, Haijun Che, Feiyang Ren +1Industrial Anomaly Detection Anomaly Localization
Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers
May 30, 2026Xubin Fang, Rick S. BlumAnomaly Localization Shapley Value Attribution
Electrical and Computer Engineering Department of Lehigh University
Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
May 28, 2026Xiaona Zhou, Muntasir Wahed, Tianjiao Yu +2Interpretable Anomaly Detection Anomaly Localization
University of Illinois Urbana-Champaign · Sandia National Laboratories
Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection
May 28, 2026Yangchen Wu, Huiqiang XieIndustrial Anomaly Detection Anomaly Localization
School of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510632, P. R. China
OpenClawBench: Benchmarking Process-side Anomalies in Real-world Agent Execution Trajectories
May 28, 2026Yibing Liu, Yangze Liu, Xiaolong Yin +4Agent Failure Analysis Anomaly Localization
School of Software, Shandong University, Jinan, China · School of Artificial Intelligence, Nanjing University; State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · Center for Medical Artificial Intelligence; Qingdao Academy of Chinese Medical Sciences; Institute of Marine Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine,2026 Qingdao 266112, China +1
Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
May 27, 2026Jungwook Seo, Minjeong Kim, Younkwan Lee +2Anomaly Localization Unsupervised Anomaly Detection
Dept. of Artificial Intelligence, Hanyang University · Global Technology Research, Samsung Electronics · Dept. of Data Science, Hanyang University
Memory-Distilled Selection for Noise-Robust Anomaly Detection
May 26, 2026Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4Industrial Anomaly Detection Data Selection
AIVEX Inc. · Northeastern University · Mitsubishi Electric Research Laboratories (MERL).
Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
May 26, 2026Yuxuan Yin, Chen He, Todd Jacobs +4Semiconductor Manufacturing Industrial Anomaly Detection
Department of Electrical and Computer Engineering, University of California Santa Barbara, CA, USA · Automotive Processing, NXP Semiconductors, TX, USA
Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection
May 25, 2026Jiaxuan Liu, Yunkang Cao, Yufeng Chen +3Industrial Anomaly Detection Anomaly Localization
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, Hunan, China
Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning
May 23, 2026Mustafa Kadhim, Viktor Rogowski, Emilia Persson +7Medical Image Anomaly Detection Anomaly Localization
Medical Radiation Physics, Lund University, Lund, Sweden. · Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden. · Medical Radiation Physics, Department of Translational Medicine, Lund University, Malmö, Sweden +4
Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models
May 21, 2026Shengzhe Chen, Mehrdad Moradi, Kamran Paynabar +1Flow Matching Industrial Anomaly Detection
1Arizona State University · 2Georgia Institute of Technology
Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection
May 19, 2026Albert Schotschneider, Daniel Bogdoll, Svetlana Pavlitska +2Autonomous Driving Perception Anomaly Localization
FZI Research Center for Information Technology · KIT Karlsruhe Institute of Technology
JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
May 19, 2026Hyunju Kang, Woohyun Lee, Jaewon Kim +1Industrial Anomaly Detection Anomaly Localization
Sungkyunkwan University · Seoul National University
POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection
May 18, 2026Suofei Zhang, Yaxuan Zheng, Haifeng HuAnomaly Localization Time-Series Anomaly Detection
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China · National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track
May 14, 2026Lukas Roming, Felix Lehnerer, Jonas V. Funk +4OOD Generalization Industrial Anomaly Detection
Fraunhofer IOSB, Fraunhoferstr. 1, 76131 Karlsruhe, Germany · Independent Research
Weakly-Supervised Spatiotemporal Anomaly Detection
May 13, 2026Urvi Gianchandani, Praveen Tirupattur, Mubarak ShahAnomaly Localization Weakly Supervised Video Anomaly Detection
University of Texas at Dallas · University of Central Florida
Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models
May 12, 2026Furkan Mumcu, Michael J. Jones, Anoop Cherian +1Large Vision-Language Models Anomaly Localization
University of South Florida · Mitsubishi Electric Research Laboratories (MERL)
MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection
May 11, 2026Xiran Zhao, Jing Jin, Yan Bai +6Multi-View Learning Industrial Anomaly Detection
ShanghaiTech University · Tsinghua University · Meituan Inc. +1
Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation
May 9, 2026Lei Ma, Suhani Chaudhary, Ethan Shanbaum +5Online Anomaly Detection Interpretable Anomaly Detection
Worcester Polytechnic Institute, USA · UC Riverside, USA · University of Nevada, Las Vegas, USA
Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation
May 9, 2026Yutszyuk Wong, Wentai Wu, Yuen-Ying Yeung +1Online Anomaly Detection Anomaly Localization
Jinan University · Guangzhou, China · South China University of Technology
Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
May 9, 2026Mariafrancesca Patalano, Giovanna Capizzi, Kamran PaynabarPoint Cloud Anomaly Detection Anomaly Localization
Department of Statistical Sciences, University of Padua · School of Industrial and Systems Engineering, Georgia Institute of Technology
LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)
May 6, 2026Wei Luo, Yiting Lu, Xin Li +32Physical Consistency in Video Generation Anomaly Localization
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
May 6, 2026Bernhard Kainz, Johanna P Mueller, Matthew Baugh +1Medical Image Anomaly Detection Anomaly Localization
Dept. AIBE, Friedrich–Alexander University Erlangen–Nürnberg, DE · Department of Computing, Imperial College London, UK · Technical University Munich, DE +1
CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training
May 6, 2026Yida Gu, Fakang Wang, Jianhao Fu +17Anomaly Localization Distributed Training
University of Chinese Academy of Sciences · Ant Group · Jilin University
Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video
May 2, 2026Jiantang HuangTemporal Video Grounding Large Vision-Language Models
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
Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships
Apr 29, 2026Yuhan Wang, Ruobing Yan, Zhe Su +3Anomaly Localization Reconstruction-Based Anomaly Detection
Columbia University, New York, USA · Georgetown University, Washington, D.C., USA · Pepperdine University, Malibu, USA +2
Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference
Apr 26, 2026Xiaowei Mao, Bowen Sui, Weijie Zhang +7Efficient VLM Inference Anomaly Localization
School of Computer Science and Technology, Beijing Jiaotong University · 2Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education · 3Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence

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NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks

Fabien Chraim, Jian Zhang, Dominik Janzing, Xiang Song+2

Amazon Web Services, Seattle, Washington, USA

Anomaly Localization Causal Inference Root Cause Analysis Counterfactual Reasoning Fault Diagnosis

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Can a learned model capture how faults propagate through a large-scale network and use this knowledge to causally attribute customer impact to its underlying root cause? Existing root cause analysis techniques often rely on static rules, correlation heuristics, or topology-local reasoning, which struggle to generalize in dynamic environments where faults propagate across complex physical and logical dependencies. We present NetCause, a self-supervised learning-based framework that models network incidents as graph-temporal processes and uses counterfactual simulation to rank candidate root causes. This approach produces an interpretable ranking of root cause hypotheses and integrates naturally with operator-defined mitigation and remediation actions. We train the model on over 1,500 incidents collected over six months from a leading cloud provider's production network and evaluate it on 31 expert-labeled incidents. NetCause consistently improves root cause ranking quality in the regime most relevant to operational decision-making, achieving a 16.1% accuracy improvement over a rule-based heuristic baseline. While training is computationally intensive, inference is lightweight, requiring only seconds of GPU runtime per incident (well below typical telemetry collection latencies).

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