AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
arXiv:2602.13807v1 Announce Type: new 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
arXiv:2602.13807v1 Announce Type: new 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 workflow is supported by a set of reusable tool engines, enabling context-aware diagnostic analysis. A key design of AnomaMind is an explicitly designed hybrid inference mechanism for tool-augmented anomaly detection. In this mechanism, general-purpose models are responsible for autonomous tool interaction and self-reflective refinement, while core anomaly detection decisions are learned through reinforcement learning under verifiable workflow-level feedback, enabling task-specific optimization within a flexible reasoning framework. Extensive experiments across diverse settings demonstrate that AnomaMind consistently improves anomaly detection performance. The code is available at https://anonymous.4open.science/r/AnomaMind.
Executive Summary
The article 'AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning' introduces a novel framework for time series anomaly detection that addresses the limitations of existing methods. Traditional approaches often treat anomaly detection as a static prediction task, leading to challenges in handling context-dependent and diverse anomaly patterns. AnomaMind reformulates this process as a sequential decision-making task, incorporating adaptive feature preparation, reasoning-aware detection, and iterative refinement. The framework utilizes a structured workflow with reusable tool engines to enable context-aware diagnostic analysis. Experimental results demonstrate significant improvements in anomaly detection performance across various settings.
Key Points
- ▸ AnomaMind reformulates anomaly detection as a sequential decision-making process.
- ▸ The framework incorporates adaptive feature preparation and reasoning-aware detection.
- ▸ Iterative refinement through self-reflection enhances detection accuracy.
- ▸ Reusable tool engines support context-aware diagnostic analysis.
- ▸ Experiments show consistent performance improvements in diverse settings.
Merits
Innovative Approach
AnomaMind's agentic framework represents a significant advancement in anomaly detection by incorporating sequential decision-making and iterative refinement, addressing the limitations of static prediction models.
Adaptive Feature Preparation
The use of multi-turn tool interactions for adaptive feature preparation allows the framework to handle context-dependent and diverse anomaly patterns more effectively.
Context-Aware Analysis
The reusable tool engines enable context-aware diagnostic analysis, enhancing the framework's ability to localize and refine anomalous intervals.
Demerits
Complexity
The complexity of the framework may pose challenges in implementation and scalability, particularly for organizations with limited computational resources.
Reinforcement Learning Dependency
The reliance on reinforcement learning for core anomaly detection decisions may introduce additional training complexities and potential biases.
Expert Commentary
AnomaMind represents a significant step forward in the field of time series anomaly detection. By reformulating the problem as a sequential decision-making process, the framework addresses key limitations of traditional approaches, particularly in handling context-dependent and diverse anomaly patterns. The incorporation of adaptive feature preparation and reasoning-aware detection, supported by reusable tool engines, enhances the framework's ability to localize and refine anomalous intervals. The use of reinforcement learning for core anomaly detection decisions, while innovative, introduces potential challenges in training and bias mitigation. The extensive experimental results demonstrate the framework's effectiveness across diverse settings, highlighting its potential for real-world applications. However, the complexity of the framework may pose implementation challenges, particularly for organizations with limited resources. Overall, AnomaMind's contributions are substantial and likely to influence future research and practical applications in anomaly detection.
Recommendations
- ✓ Further research should focus on simplifying the framework to enhance scalability and ease of implementation.
- ✓ Investigation into alternative training methods for reinforcement learning could mitigate potential biases and improve detection accuracy.
