arXiv Machine Learning August 21, 2026 By Tu Anh Hoang Nguyen, Dang Nguyen, Thuc Duy Le, Trung Le, Sunil Gupta

LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2608. 19463v1 Announce Type: new Abstract: Anomaly detection in tabular data is challenging because abnormal samples often arise as violations of cross-feature dependencies rather than simple marginal deviations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 30

RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection

arXiv:2606. 28970v1 Announce Type: cross Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient.

By Quanling Zhao, Jiaying Yang, Ye Tian, Josh Victoria, Zhijun Wang, Pietro Mercati, Onat Gungor, Tajana Rosing

arXiv Machine Learning

Jun 11

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

arXiv:2606. 11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance.

By Dayananda Herurkar, Federico Raue, Joachim Folz, J\"orn Hees, Andreas Dengel

fine-tuning efficiency

arXiv AI

Jul 24

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.

By L\'ea Billet (LAAS, INSA Toulouse, ANITI), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS), Alexandre Gaffet

diffusion benchmarks

arXiv AI

Jul 22

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.

By Kamil Faber, Mateusz Smendowski, Roberto Corizzo

benchmarks

arXiv Machine Learning

Jun 10

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.

By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao

agents safety

arXiv Machine Learning

Aug 12

BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

arXiv:2608. 10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings.

By Jiaqi Qiu, Rob Goedhart, Jannis Kurtz, Inez M. Zwetsloot

One email a morning, machine-written

Related stories

RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis