arXiv AI June 30, 2026 By Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.

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 AI.

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

arXiv AI

Jun 12

Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

arXiv:2606. 13311v1 Announce Type: cross Abstract: Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where rare regimes can be critical information.

By Yongmin Kim, ByeongHoon Jeon, Sungil Kim

safety

arXiv AI

2d ago

Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led t...

By Roberto Stanzione, Jules Barbe, Magali Parrino, J\'er\'emie Fourmann, Paul Boniol

llms benchmarks safety

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

Aug 12

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data

arXiv:2608. 10249v1 Announce Type: new Abstract: We present a scalable framework for unsupervised clustering of maritime trajectories derived from terabyte-scale Automatic Identification System (AIS) archives.

By Bertram Hage, Alexander Schi{\o}tz, Felix Thomsen, Christian Rand, Peder Heiselberg

One email a morning, machine-written

Related stories

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

BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection

Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

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

STCAD: Scalable Trajectory Clustering and Anomaly Detection on Terabyte-Scale AIS Data