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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L2IR: Revealing Latent Intent in Graph Fraud Detection
May 25, 2026Jinsheng Guo, Zhenhao Weng, Yibo Liu +2Anomaly Detection
Hefei University of Technology Hefei, China
Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection
May 25, 2026Qideng Tang, Dai Chaofan, Wubin Ma +5Reconstruction-Based Anomaly Detection Time-Series Anomaly Detection
Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
May 25, 2026Yujing Liu, Yixin Liu, Yu Zheng +3Domain Generalization Anomaly Detection
Griffith University, Gold Coast, Australia · Tongji University, Shanghai, China
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
When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection
May 25, 2026Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2Counterfactual Data Augmentation Anomaly Detection
School of Electrical Engineering and Robotics, Faculty of Engineering, −Queensland University of Technology, Brisbane, Queensland, Australia
Stream Assembly Is an Uncontrolled Treatment in Streaming Intrusion-Detection Benchmarks
May 23, 2026Michel A. YoussefBenchmark Design Network Intrusion Detection
Independent Researcher, Beirut, Lebanon
FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
May 23, 2026Ruihao Xu, Yong Liu, Yansong TangObject Detection Semi-Supervised Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University
Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
May 23, 2026Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2OOD Detection Anomaly Detection
School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
May 23, 2026Yaoxuan Feng, Yuxin Li, Weijiang Lv +5Prototype Learning Industrial Anomaly Detection
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China.
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
May 22, 2026Chad Weatherly, Sen LinEdge Inference Industrial Anomaly Detection
University of Houston
Beyond Normal References: Discriminative Few-Shot Anomaly Detection
May 22, 2026Huan Wang, Jun Shen, Jun Yan +1Few-Shot Anomaly Detection Few-Shot Learning
Singapore Management University, Singapore · University of Wollongong, Australia
FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
May 21, 2026Huanchi Wang, Zihang Huang, Yifang Tian +3Fault Detection Mixture-of-Experts Models
Department of Electrical and Computer Engineering University of Toronto, Toronto, Ontario
Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
May 21, 2026Adis Alihodžić, Eva Tuba, Milan TubaCyber-Physical Systems Cybersecurity
Department of Mathematical and Computer Sciences, University of Sarajevo, Zmaja od Bosne 33–35, Sarajevo, 71000, Bosnia & Herzegovina · Singidunum University, Danijelova 32, Belgrade, 11000, Serbia · Trinity University, 1 Trinity Pl, San Antonio, 78212, Texas, USA +1
Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection
May 21, 2026Giancarlo P. Gamberi, Calebe P. BianchiniMetaheuristic Optimization Anomaly Detection
Mackenzie Presbyterian University
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
May 21, 2026Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +2Federated Learning Aggregation Non-IID Federated Learning
Universitat Politècnica de València Valencia, Spain · Universitat Jaume I Castelló de la Plana, Spain
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
May 20, 2026Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2Post-Hoc Calibration Autonomous Racing
Trustworthy Engineered Autonomy (TEA) Lab, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity
May 20, 2026Kaifeng Wei, Teng Liu, Liang Dong +2Zero-Shot Anomaly Detection Cross-Dataset Generalization
School of Software Technology Zhejiang University Ningbo, China · School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou, China
VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals
May 20, 2026Joey Chan, Zhen Chen, Ershun PanPredictive Maintenance LLM Agents
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks
May 19, 2026Vaibhav ChhabraOnline Anomaly Detection Cybersecurity
USPTO Patent #64/034,500
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
May 19, 2026Sudheer Tubati, Amit GoyalEnsemble Learning Hard Negative Mining
Amazon Music Seattle, USA · Amazon Music San Francisco, USA
CAFD: Concept-Aware DNN Fault Detection using VLMs
May 19, 2026Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel BriandAnomaly Detection
School of EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada · Research Ireland Lero centre for software, University of Limerick, Ireland
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
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
May 19, 2026Wen Shi, Zhe Wang, Huafei Huang +6Graph Representation Learning Text-Attributed Graph Learning
College of Computer Science and Technology, Jilin University, Changchun 130012, China · School of Computer Science and Information Technology, Adelaide University, Adelaide SA5095, Australia · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia +1
A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
May 19, 2026João Pedro Matos-Carvalho, Laio Oriel Seman, Stefano Frizzo Stefenon +2Mixture of Experts Object Detection
LASIGE, Faculdade de Ciˆencias, Universidade de Lisboa, 1749-016 Lisboa, Portugal · Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florian´opolis, Brazil · Instituto Superior de Engenharia de Lisboa, Instituto Polit´ecnico de Lisboa, Rua Conselheiro Em´ıdio Navarro 1, 1959-007 Lisboa, Portugal +1
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
May 19, 2026Carlos A. Durán Paredes, Javier E. León Calderón, Nicolás Sánchez Perea +2Cybersecurity Quantum Machine Learning
Corporation for Aerospace Initiatives, Research and Innovation (CASIRI), Popayán, Colombia · Department of Electronics Engineering, Universidad Nacional de Colombia, Manizales, Colombia. · Department of Electronics Engineering, Universidad del Cauca, Popayán, Colombia. +1
Guard: Scalable Straggler Detection and Node Health Management for Large-Scale Training
May 18, 2026Guanliang Liu, Abhinandan Patni, Congzhu Lin +14Distributed Training Anomaly Detection
Store Foundational AI, Amazon, Seattle, WA, USA.
Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection
May 18, 2026Jiatong XieGraph Clustering Spectral Methods
Independent researcher.
Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
May 18, 2026Hang-Cheng Dong, Guodong Liu, Dong Ye +1Interpretable ML Industrial Inspection
School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China · Harbin Institute of Technology Suzhou Research Institute, Suzhou, China
MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling
May 18, 2026Kaichuan Kong, Dongjie Liu, Xiaobo Jin +1Multi-View Learning Anomaly Detection
College of Cyber Security, Jinan University, Guangzhou, China · School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China
MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
May 17, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanBenchmark Design Synthetic Data Generation
Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham, India

May 25, 2026, cs.AIJ/K move · Enter open · S save

L2IR: Revealing Latent Intent in Graph Fraud Detection

Jinsheng Guo, Zhenhao Weng, Yibo Liu, Yan Qiao+1

Hefei University of Technology Hefei, China

Anomaly Detection

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Graph fraud detection has long depended on Graph Neural Networks (GNNs) to propagate and aggregate information across relational data. A critical obstacle in practice, however, is that fraudsters frequently disguise themselves by forging numerous connections with benign users, causing fraud signals to be progressively diluted during neighborhood aggregation and undermining detection reliability. While recent efforts have used Large Language Models (LLMs) to provide rich semantic cues for fraud detection, the underlying intent behind suspicious connections remains insufficiently explored. Compounding this issue, the scarcity of annotated fraud samples makes it difficult to train detectors that remain robust under heavy camouflage. To address these gaps, we propose L2IR, an LLM-driven Latent Intent Revealing framework for graph fraud detection. By uncovering latent intent from both user behaviors and suspicious connections, L2IR extracts intent-aware representations from raw behavioral traces and reasons about the true purpose behind individual connections, effectively distinguishing supportive links from misleading ones. It further incorporates adaptive self-training to enhance robustness under limited supervision. Evaluations on two real-world datasets characterized by pervasive camouflage demonstrate that L2IR surpasses strong baselines and can function as a plug-in enhancement for a range of GNN-based detectors, improving AUPRC by up to 8.27%.

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