mlsanityData to 14 Oct 2026

ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution

arXiv 2601.13546 ↗First posted 20 Jan 2026cs.AIcited by 0

On the map: LLM reasoning → Time series reasoning

Abstract (arXiv)

LLM-driven Anomaly Detection (AD) helps enhance the understanding and explanatory abilities of anomalous behaviors in Time Series (TS). Existing methods face challenges of inadequate reasoning ability, deficient multi-turn dialogue capability, and narrow generalization. To this end, we 1) propose a multi-agent-based TS Evolution algorithm named TSEvol. On top of it, we 2) introduce the AD reasoning and multi-turn dialogue Dataset TSEData-20K and contribute the Chatbot family for AD, including ChatAD-Llama3-8B, Qwen2.5-7B, and Mistral-7B. Furthermore, 3) we propose the TS Kahneman-Tversky Optimization (TKTO) to enhance ChatAD's cross-task generalization capability. Lastly, 4) we propose a LLM-driven Learning-based AD Benchmark LLADBench to evaluate the performance of ChatAD and nine baselines across seven datasets and tasks. Our three ChatAD models achieve substantial gains, up to 34.50% in accuracy, 34.71% in F1, and a 37.42% reduction in false positives. Besides, via KTKO, our optimized ChatAD achieves competitive performance in reasoning and cross-task generalization on classification, forecasting, and imputation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 44 references that are in the map. First posted on arXiv 20 Jan 2026.

Builds on

15 of 44 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs12 Jun
  2. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning4 Dec

Other references · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  4. 2024Qwen2.5 Technical Reportcited by 4,942
  5. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  6. 2024DeepSeek-V3 Technical Reportcited by 811
  7. 2024KTO: Model Alignment as Prospect Theoretic Optimizationcited by 264
  8. 2024MMEvol: Empowering Multimodal Large Language Models with Evol-Instructcited by 42
  9. 2025ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Datasetcited by 37
  10. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 26
  11. 2025Large Language Models For Text Classification: Case Study And Comprehensive Reviewcited by 12
  12. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 8
1 more
  1. 2025ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Modelscited by 1

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  1. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
  2. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  3. 2025Refining Time Series Anomaly Detectors using Large Language Models
  4. 2026LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection
  5. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Models
  6. 2024Can LLMs Serve As Time Series Anomaly Detectors?
  7. 2026Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
  8. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?