Anomaly Detection

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40 papers in the last four weeks, up 33% on the four weeks before. 0.4% of all new papers.

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A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision
Jun 1, 2026Stefano Samele, Eugenio Lomurno, Teodora Jovanovic +3VLM Evaluation Industrial Anomaly Detection
Politecnico di Milano, AIRLab, Milan, Italy · S&H – Software & Hardware, Milan, Italy
SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems
Jun 1, 2026Eric LiangCybersecurity Anomaly Detection
Oracle
Agent System Operations: Categorization, Challenges, and Future Directions
Jun 1, 2026Zexin Wang, Changhua Pei, Yuanhao Liu +10Agent Failure Analysis AI Agent Monitoring
CNIC, CAS · Hangzhou Institute for Advanced Study, UCAS · Singapore Management University +1
Description and Discussion on DCASE 2026 Challenge Task 2: Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Jun 1, 2026Tomoya Nishida, Noboru Harada, Daiki Takeuchi +6Industrial Anomaly Detection Unsupervised Anomaly Detection
Hitachi, Ltd., Japan · NTT, Inc., Japan · SB Intuitions Corp. +1
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
State Machine Guided Multi-Relational Synthetic Data from Logs for Anomaly Detection
May 30, 2026Aja Khanal, Apurva NarayanSynthetic Data Augmentation Online Anomaly Detection
University of Western Ontario London, Canada
DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
May 30, 2026Arun SharmaRemote Sensing Image Understanding Multimodal Anomaly Detection
University of Minnesota, Twin Cities
Benchmarking Recursive-Collapse Warning Claims Under Matched False-Positive Control
May 29, 2026David MullettBenchmark Design Model Collapse
Independent Researcher
Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
May 29, 2026Yilin Liu, Hongchao Zhang, Taylor T. Johnson +2Spectral Methods Spatiotemporal GNNs
College of Connected Computing, Vanderbilt University, Nashville, USA · Department of Civil and Environmental Engineering, Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
May 29, 2026Jyotirmoy Singh, Anushka Roy, Shreea Bose +1Interpretable ML Interpretable Anomaly Detection
A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)
May 29, 2026Vivek Kumar SharmaNetwork Intrusion Detection Encrypted Network Traffic Analysis
Palo Alto Networks
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
May 29, 2026Seongheon Park, Wendi Li, Changdae Oh +4Robot Failure Detection Fault Detection
University of Wisconsin–Madison · Georgia Institute of Technology
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
May 28, 2026Yi Zhang, Jiawen Zhu, Lele Fu +1Few-Shot Anomaly Detection Tool Use in VLMs
Singapore Management University, Singapore · Sun Yat-sen University, China
Masked Diffusion Modeling for Anomaly Detection
May 28, 2026Lixing Zhang, Yuchen Liang, Liyan XieMasked Diffusion Models Tabular Anomaly Detection
University of Minnesota · Ohio State University
A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
May 28, 2026Ismet Gocer, Zakirul Bhuiyan, Raza Hasan +1Unsupervised Anomaly Detection Automated Evaluation
Southampton Solent University School of Technology and Maritime Industries Southampton, United Kingdom
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
May 28, 2026Runang He, Tongya Zheng, Huiling Peng +6Temporal GNNs Test-Time Adaptation
State Key Laboratory of Blockchain and Data Security, Zhejiang University · Zhejiang Provincial Engineering Research Center for Real-Time SmartTech in Urban Security Governance, Hangzhou City University · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
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
SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction
May 28, 2026Arunkumar RamachandranHealthcare Time Series Classification
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
Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
May 27, 2026Junhao Wei, Yanxiao Li, Bidong Chen +9Multi-View Learning Time-Series Anomaly Detection
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · 2Macao Polytechnic University, Macao SAR, China
Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems
May 26, 2026Abile Jean, Kuniyilh SCyber-Physical Systems Backdoor Attacks
Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation
May 26, 2026Zijian Du, Oleg RybakovEfficient Neural Network Inference Image Segmentation
NVIDIA
Learning Dynamic Graph Representations through Timespan View Contrasts
May 26, 2026Yiming Xu, Zhen Peng, Bin Shi +2Graph SSL Dynamic Graph Learning
School of Computer Science and Technology, Xi’an Jiaotong University, P.R. China · School of Distance Education, Xi’an Jiaotong University, P.R. China
Generalist Graph Anomaly Detection via Prototype-Based Distillation
May 26, 2026Yiming Xu, Zihan Chen, Zhen Peng +4Unsupervised Anomaly Detection Zero-Shot Anomaly Detection
School of Computer Science and Technology, Xi’an Jiao-tong University, Xi’an, China · National Engineering Research Center for Visual Information and Applications, Xi’an, China · University of Virginia, Charlottesville, USA +3
Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
May 26, 2026Tairan Huang, Qiang Chen, Yili Wang +4Agentic Workflows Anomaly Detection
Central South University · The Hong Kong University of Science and Technology · The Hong Kong University of Science and Technology (Guangzhou)
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
DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine
May 26, 2026Yuxin Yang, Limei Hu, Feng ChenGraph Signal Processing Unsupervised Anomaly Detection
College of Artificial Intelligence Southwest University Chongqing, China
Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
May 25, 2026Xu Yao, Siyuan Zhou, Zhenbo Wu +6Learning with Noisy Labels Anomaly Detection
Shanghai University of Finance and Economics · Ant Group · Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics

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A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar+2

Politecnico di Milano, AIRLab, Milan, Italy · S&H – Software & Hardware, Milan, Italy

VLM Evaluation Industrial Anomaly Detection Multimodal Anomaly Detection Zero-Shot Anomaly Detection Anomaly Detection

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Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual input alongside the image and are presented as enabling text-guided zero- and few-shot inspection. Yet these methods are evaluated with protocols inherited from unimodal benchmarks that hold the textual condition constant and therefore cannot measure whether language conditions the decision; whether reported gains reflect text guidance or strong pretrained visual features remains open. We introduce Text-Guided Anomaly Detection (TGAD), a structured benchmark that progressively increases the functional role of language across three scenarios: a controlled prompt-sensitivity setting on MVTec AD; a component-tagged extension of MVTec AD that requires the model to restrict its assessment to an instructed part; and the new Assembled Panel Dataset (APD), a realistic industrial setting that requires both defect-type and component-location knowledge. We evaluate one representative model per paradigm: generative large vision-language, training-free discriminative, and embedding-adaptive discriminative. In all three, the textual interface conditions the decision only superficially: prompt content is absorbed unless the object noun is removed (the generative model's I-AUROC drops from 97.4 to 82.6); component-level instructions do not constrain the decision once defects outside the instructed part are admitted as normal (from 90.3 to 66.3); and when both combine on APD, image-level discrimination collapses below the MVTec level, in one case below chance (71.2, 50.5, 31.5). These results suggest that standard benchmarks overstate the text-guided capabilities of current multimodal anomaly detection systems, and that a protocol of this kind is a prerequisite for models that can be reliably controlled through language for industrial deployment.

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