arXiv AI June 2, 2026 By Junru Zhang, Lang Feng, Haoran Shi, Xu Guo, Han Yu, Yabo Dong, Duanqing Xu

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

arXiv:2602. 08868v2 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional, detailed reasoning, which is vital for understanding complex time-series data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 5

TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series

arXiv:2608. 03391v1 Announce Type: new Abstract: Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, financial services, and logistics, where brief evidence may hide inside long spans of high-frequency data.

By Nicolas Zumarraga, Lorenzo Steno, Ning Wang, Max Rosenblattl, Thomas Kaar, Maxwell A. Xu, Kevin O'Sullivan, Markus Kreft, Elgar Fleisch, Paul Schmiedmayer, Patrick Langer, Robert Jakob

llms agents reinforcement-learning 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

6d ago

JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

arXiv:2608. 11801v1 Announce Type: new Abstract: Multivariate time-series anomaly prediction aims to identify whether and when anomalies will occur over a future horizon from historical observations.

By Yian Wei, Yuanyuan Yao, Lu Chen, Xiangmin Zhou, Tianyi Li

benchmarks

arXiv AI

Jul 9

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

arXiv:2603. 11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood.

By David Baumgartner, Eliezer de Souza da Silva, I\~nigo Urteaga

diffusion

arXiv Machine Learning

Jun 17

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604. 17616v3 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems.

By Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker, Jason Rambach

rag benchmarks

arXiv AI

Jun 2

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.

By Yaxuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yilei Shao, Stefan Zohren, Anna Vettoruzzo, Joaquin Vanschoren, Ming Jin, Qingsong Wen

llms agents benchmarks

Related stories

TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series

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

JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning