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[2602.13807] AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
Summary
AnomaMind presents a novel framework for time series anomaly detection, enhancing traditional methods by incorporating tool-augmented reasoning and adaptive feature preparation to improve decision-making in complex environments.
Why It Matters
Time series anomaly detection is essential across various industries, including finance and healthcare. AnomaMind's approach addresses limitations of existing methods, offering a more dynamic and context-aware solution that can lead to better outcomes in critical applications.
Key Takeaways
- AnomaMind reformulates anomaly detection as a sequential decision-making process.
- It utilizes multi-turn tool interactions for adaptive feature preparation.
- The framework incorporates self-reflection to refine anomaly detection decisions.
- Extensive experiments show consistent performance improvements over traditional methods.
- The code for AnomaMind is publicly available, promoting further research and application.
Computer Science > Machine Learning arXiv:2602.13807 (cs) [Submitted on 14 Feb 2026] Title:AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning Authors:Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao View a PDF of the paper titled AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning, by Xiaoyu Tao and 4 other authors View PDF HTML (experimental) 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. However, most existing methods frame anomaly detection as a purely discriminative prediction task with fixed feature inputs, rather than an evidence-driven diagnostic process. As a result, they often struggle when anomalies exhibit strong context dependence or diverse patterns. We argue that these limitations stem from the lack of adaptive feature preparation, reasoning-aware detection, and iterative refinement during inference. To address these challenges, we propose AnomaMind, an agentic time series anomaly detection framework that reformulates anomaly detection as a sequential decision-making process. AnomaMind operates through a structured workflow that progressively localizes anomalous intervals in a coarse-to-fine manner, augments detection through multi-turn tool interactions for adaptive feature preparation, and refines anomaly decisions via self-reflection. The wor...