Contextual Anomaly Detection

Latest papers

14 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

ReForest: Interpretability Meets Reasoning in Isolation Forest
Oct 27, 2026Juyoung Seo, Geonwoo Bang, Dongho Kim +1Contextual Anomaly Detection Anomaly Detection
Department of Computer Education, Sungkyunkwan University, Seoul, Korea · Department of Immersive Media Engineering, Sungkyunkwan University, Seoul, Korea · Department of Computer Education/Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Korea
Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision
Oct 5, 2026Tian Lan, Yifei Gao, Yimeng Lu +5LLM-Based Time Series Anomaly Detection Time-Series Anomaly Detection
Department of Industrial Engineering, Tsinghua University · Huawei
Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing
Sep 27, 2026Yifei Gao, Tian Lan, Yimeng Lu +5Time-Series Anomaly Detection Contextual Anomaly Detection
Department of Industrial Engineering, Tsinghua University · Huawei
Localizing Global Discrepancies: Marginal Contributions and Contextual Anomaly Detection
Aug 28, 2026Tommaso dorigoContextual Anomaly Detection Anomaly Localization
INFN, Sezione di Padova, Padova, Italy · Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden
Context-structured Video Anomaly Detection with Large Vision-Language Models
Jul 21, 2026Dongjun Kim, Changjae Oh, Andrea Cavallaro +1Reference-Based Video Anomaly Detection Video Anomaly Detection
Yonsei University, Seoul, Republic of Korea · Queen Mary University of London, London, United Kingdom · EPFL, Lausanne, Switzerland
LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning
Jul 14, 2026Shiyi Mu, Xujie Chen, Shugong XuContextual Anomaly Detection Autonomous Driving Perception
Shanghai University, Shanghai, China · Xi’an Jiaotong-Liverpool University, Suzhou, China
Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection
Jun 11, 2026Yongmin Kim, ByeongHoon Jeon, Sungil KimContextual Anomaly Detection Time-Series Anomaly Detection
Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.
LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
May 14, 2026Mitchell Piehl, Muchao YeMultimodal Anomaly Detection Interpretable Anomaly Detection
The University of Iowa, Iowa City IA 52245, USA
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
May 1, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Reconstruction-Based Anomaly Detection Graph Attention Networks
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Apr 29, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Reconstruction-Based Anomaly Detection Graph Attention Networks
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Apr 14, 2026Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6Contextual Anomaly Detection Anomaly Detection
Aalborg University, Denmark · Pioneer Centre for Artificial Intelligence, Denmark · Technical University of Denmark, Denmark +3
TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference
Mar 15, 2026Patryk Marszałek, Tomasz Kuśmierczyk, Marek ŚmiejaContextual Anomaly Detection Tabular Anomaly Detection
Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
Feb 24, 2026Qingsong Zou, Zhi Yan, Zhiyao Xu +3Contextual Anomaly Detection Anomaly Detection
Tsinghua Shenzhen International Graduate School Shenzhen, China · Jilin University Jilin, China · The Chinese University of Hong Kong Hong Kong, China +1
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
Feb 15, 2026Nicolas Zumarraga, Thomas Kaar, Ning Wang +12Contextual Anomaly Detection Time Series Reasoning
1Agentic Systems Lab, ETH Zurich · 2Stanford University · 3Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich +4

Oct 27, 2026J/K move · Enter open · S save

ReForest: Interpretability Meets Reasoning in Isolation Forest

Juyoung Seo, Geonwoo Bang, Dongho Kim, Moohong Min

Department of Computer Education, Sungkyunkwan University, Seoul, Korea · Department of Immersive Media Engineering, Sungkyunkwan University, Seoul, Korea · Department of Computer Education/Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Korea

Contextual Anomaly Detection Anomaly Detection Interpretable Anomaly Detection Isolation Forest Ensemble Learning

Open paper AskPDF arXiv

Statistical anomaly detection methods like Isolation Forest are efficient but suffer from a critical semantic gap. They identify numerical rarities but fail to understand the contextual meaning of data, often missing subtle, context-dependent anomalies. Conversely, Large Language Models possess strong semantic reasoning capabilities but are prohibitively expensive and slow for large-scale deployment. To bridge this gap, we propose ReForest, a novel framework that synergizes the statistical efficiency of Isolation Forest with the reasoning power of LLMs. ReForest transforms the latent decision paths of Isolation Forest into natural-language explanations, enabling an LLM to refine anomaly detection through an uncertainty-aware dynamic ensemble. This approach allows the model to adaptively weigh statistical and semantic signals based on uncertainty-driven instance-level confidence. Extensive experiments on four real-world datasets (Healthcare, Finance) demonstrate that ReForest significantly outperforms baselines, achieving an average 21% improvement in Recall and a 10% gain in F1-score. Our framework not only improves detection performance but also enhances model transparency by grounding semantic narratives in statistical evidence, providing interpretable rationales essential for high-stakes decision-making.