| 1 | Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data | Lamine Diop, Marc Plantevit | cs.LG | 2026-09-03 |
| 2 | Witnesses Explain Anomalies | Lamine Diop | cs.LG | 2026-09-03 |
| 3 | A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites | Eliud Nyakweba Koto, Jaco du Toit, Adham Stoltz +1 | cs.LG | 2026-09-03 |
| #4 | PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection | Xiaoyu Yang, Qixing Wu, Huixian Zhao +1 | cs.CV | 2026-09-03 |
| #5 | An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data | Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah | cs.LG | 2026-09-03 |
| #6 | Neural-Collapse-guided Task-Free Continual Anomaly Detection | Xiaotong Kong, Chaoyang Song, Ziai Zhou +3 | cs.CV | 2026-09-03 |
| #7 | RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection | Taufikur Rahman Fuad, Md Abrar Jahin, Amir Hussain | cs.LG | 2026-09-02 |
| #8 | Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs | Philipp Schlinge, Jean-Luc Schnipper, Martin Atzmueller | cs.SI | 2026-09-02 |
| #9 | DPA: Decoupling Product-Agnostic Anomaly Representations for Zero-shot Anomaly Generation | Hang Yao, Yansheng Fu, Ming Liu +4 | cs.CV | 2026-09-02 |
| #10 | What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models | Sharon S. Musa, Fereshteh Forghani, Harrish Thasarathan +3 | cs.CV | 2026-09-01 |
| #11 | A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data | Andres F. Monsalve, Hernan A. Moreno, Christian D. Kummerow | physics.ao-ph | 2026-09-01 |
| #12 | CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection | Tian Tian, Shuaicheng Niu, Hao Kuang +3 | cs.LG | 2026-09-01 |
| #13 | Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP-Based Semantic Scoring | Vanodhya G. Warnasooriya, Amir Hajian, Watchara Ruangsang +1 | cs.CV | 2026-08-31 |
| #14 | Generative multi-domain transfer learning for fault detection in data-scarce wind turbines | Stefan Jonas, Angela Meyer | cs.LG | 2026-08-31 |
| #15 | InspectorGPT: A Comparative Reasoning Enhanced VLM for Comprehensive Industrial Anomaly Detection | Weifei Chen, Honghao Zhang, Zhiyuan You +1 | cs.CV | 2026-08-30 |
| #16 | Detect Before You Attribute: Cascade Failure Attribution for Multi-Agent Systems | Jiayi Zhang, Zexin Wang, Degang Sun +4 | cs.AI | 2026-08-30 |
| #17 | Forward-Deployed Full-Stack Engineering for Autonomous Cloud MLOps | Sagar Srinivas Sakhinana, Venkataramana Runkana | cs.MA | 2026-08-30 |
| #18 | Co-Evolutionary Prompt Optimization with Cross-Category Transfer for Zero-Shot Anomaly Detection | Sisi Zhu, Changwei Yu, Renshuai Tao +1 | cs.CV | 2026-08-29 |
| #19 | NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift | Dat Cao, Son Nghiem, Phan Nguyen +2 | cs.CV | 2026-08-29 |
| #20 | Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection | Tommaso dorigo | stat.ML | 2026-08-28 |
| #21 | ShiftSplit-AD: Separating Domain Shift from Defects in Foundation-Feature Visual Anomaly Detection | Muhamathu Ameer Ali Aacaas Muhamath | cs.CV | 2026-08-27 |
| #22 | TraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attribution | Tommaso Bendinelli, Artur Dox, Christian Holz | cs.LG | 2026-08-27 |
| #23 | Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection | Xiang Wang, Zhijun Cheng, Zhenyu Meng | cs.AI | 2026-08-27 |
| #24 | GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment | Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng +2 | cs.CV | 2026-08-27 |
| #25 | LLM Agents for Time-Series: A Survey | Yilong Chen, Xiao Qin, Chenghao Liu +3 | cs.AI | 2026-08-26 |
| #26 | See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection | Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu +2 | cs.CV | 2026-08-25 |
| #27 | Strictly Causal Streaming Video Anomaly Detection with a Theoretically-Grounded State-Space Core | Yogesh Kumar | cs.AI | 2026-08-25 |
| #28 | Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection | Matteo Cardoni, Sam Leroux | cs.LG | 2026-08-25 |
| #29 | When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning | Hanqiu Peng, Jianlong Lu, Ying Chen | quant-ph | 2026-08-25 |
| #30 | Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection | Jungwook Seo, Sangwon Son, Minjeong Kim +3 | cs.LG | 2026-08-25 |
| #31 | STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning | Ashlinder Kaur, Purnima Murali Mohan, Zengxiang Li +1 | cs.CR | 2026-08-25 |
| #32 | DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection | Wenyang Liu, Tianyi Liu, Dongshuo Zhang +2 | cs.CV | 2026-08-24 |
| #33 | Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination | Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2 | cs.CR | 2026-08-24 |
| #34 | What's the Catch? Evaluating Temporal Consistency in Vision-Language Models | Marek Hradil, Danae Sánchez Villegas | cs.CL | 2026-08-24 |
| #35 | RAD: Rule-Augmented Relational Anomaly Detection | Noah Dahle, Anne Tumlin, Ngoc Tran +2 | cs.LG | 2026-08-24 |
| #36 | What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection | Joongwon Chae, Runming Wang, Peiwu Qin | cs.CV | 2026-08-24 |
| #37 | What Memory Composition Does Not Tell Us About Anomaly Detection | Joongwon Chae, Runming Wang, Peiwu Qin | cs.CV | 2026-08-24 |
| #38 | Quality Inspection of Printed Circuit Board Pin Insertion via Semantic Segmentation and Board-Level Feature Extraction | Nils Rabeneck, André Kiunke, Nicole Hoess +1 | cs.CV | 2026-08-24 |
| #39 | GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing | Sosmita Paul, Krishna Roy | cs.CV | 2026-08-24 |
| #40 | Multi-Modal Anomaly Detection: A Survey | Xudong Mou, Zexin Wu, Chuan Luo +4 | cs.LG | 2026-08-24 |
| #41 | Lightweight Multi-scale Hierarchical Anomaly Detection and Localization for Geospatial Big Data Applications at the Edge | Thomas Benton Townsend, Joshua Bean, Benjamin K Tkach +2 | eess.SP | 2026-08-23 |
| #42 | GCA: Global Centroid Alignment in Federated Learning | Jong-Ik Park, Harry Jiang, Logan Blakely +3 | cs.LG | 2026-08-23 |
| #43 | Frame-Level Evaluation in Weakly Supervised Video Anomaly Detection Mostly Measures Video-Level Ranking | Inpyo Song, Jangwon Lee | cs.CV | 2026-08-22 |
| #44 | ChequeMark: An Ensemble Machine Learning Framework for After-Hours Business Deposit Fraud Detection | Ann Youduo Xu, Emily Yu, Justin Leski +1 | cs.LG | 2026-08-21 |
| #45 | TRACE-C: Rank-Calibrated Relational Anomaly Detection for Multi-Stream Operational Telemetry | Matthew Faucher | cs.LG | 2026-08-21 |
| #46 | A VLM Answer Is Not an Anomaly Score: Rank Compression in Training-Free Video Anomaly Detection | Inpyo Song, Jangwon Lee | cs.CV | 2026-08-21 |
| #47 | Making Deployments Safe at Meta: Health Checks for Continuous Change-Safety | Prakash KL, Anton Korenkov, Uttam Thakore +1 | cs.SE | 2026-08-20 |
| #48 | STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection | Jakub Micorek, Mateusz Koziński, Horst Possegger | cs.CV | 2026-08-20 |
| #49 | From Noise to Signal: Improving Security Log Anomaly Detection Using LLMs with Endpoint-Specific Logs | Christopher Henshaw, Gour Karmakar | cs.CR | 2026-08-20 |
| #50 | Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection | Jialun Zheng, Hanchen Yang, Jiannong Cao +3 | cs.LG | 2026-08-20 |