mlsanityData to 13 Oct 2026

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

arXiv 2602.08868 ↗First posted 9 Feb 2026cs.LG cs.AIcited by 0

On the map: LLM reasoning → Time series reasoning

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Abstract (arXiv)

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. We present AnomSeer to address this by reinforcing the model to ground its reasoning in precise, structural details of time series, unifying anomaly classification, localization, and explanation. At its core, an expert chain-of-thought trace is generated to provide a verifiable, fine-grained reasoning from classical analyses (e.g., statistical measures, frequency transforms). Building on this, we propose a novel time-series grounded policy optimization (TimerPO) that incorporates two additional components beyond standard reinforcement learning: a time-series grounded advantage based on optimal transport and an orthogonal projection to ensure this auxiliary granular signal does not interfere with the primary detection objective. Across diverse anomaly scenarios, AnomSeer, with Qwen2.5-VL-3B/7B-Instruct, outperforms larger commercial baselines (e.g., GPT-4o) in classification and localization accuracy, particularly on point- and frequency-driven exceptions. Moreover, it produces plausible time-series reasoning traces that support its conclusions.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 38 of its 57 references that are in the map. First posted on arXiv 9 Feb 2026.

Builds on

38 of 57 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning16 Jun
  2. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs12 Jun
  3. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation3 Feb
  4. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning4 Dec
  5. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization9 Nov

Other references · 33

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,091
  2. 2025Qwen2.5-VL Technical Reportcited by 5,499
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,295
  4. 2020Gradient Surgery for Multi-Task Learningcited by 1,225
  5. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 623
  6. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 468
  7. 2025Group-in-Group Policy Optimization for LLM Agent Trainingcited by 457
  8. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 446
  9. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 390
  10. 2025SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolutioncited by 252
  11. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  12. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
21 more
  1. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 94
  2. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 93
  3. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  4. 2024MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistancecited by 73
  5. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 60
  6. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  7. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  8. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 30
  9. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 28
  10. 2024Distributional Preference Alignment of LLMs via Optimal Transportcited by 26
  11. 2025Time-R1: Towards Comprehensive Temporal Reasoning in LLMscited by 23
  12. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  13. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?cited by 12
  14. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 11
  15. 2025TimesBERT: A BERT-Style Foundation Model for Time Series Understandingcited by 10
  16. 2025Inferring Events from Time Series using Language Modelscited by 10
  17. 2024Rethinking Optimal Transport in Offline Reinforcement Learningcited by 9
  18. 2025Stackelberg Self-Annotation: A Robust Approach to Data-Efficient LLM Alignmentcited by 9
  19. 2025Optimal Transport-Based Token Weighting scheme for Enhanced Preference Optimizationcited by 7
  20. 2025APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transportcited by 6
  21. 2025Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detectioncited by 1

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  1. 2024Can LLMs Serve As Time Series Anomaly Detectors?
  2. 2026ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution
  3. 2025Refining Time Series Anomaly Detectors using Large Language Models
  4. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Models
  5. 2025RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
  6. 2026Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
  7. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
  8. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy