mlsanityData to 7 Oct 2026

Visual Reasoning over Time Series via Multi-Agent System

arXiv 2602.03026 ↗First posted 3 Feb 2026cs.AI cs.MAcited by 1

On the map: LLM reasoning → Time series reasoning

also in 1 more
Abstract (arXiv)

Time series analysis underpins many real-world applications, yet existing time-series-specific methods and pretrained large-model-based approaches remain limited in integrating intuitive visual reasoning and generalizing across tasks with adaptive tool usage. To address these limitations, we propose MAS4TS, a tool-driven multi-agent system for general time series tasks, built upon an Analyzer-Reasoner-Executor paradigm that integrates agent communication, visual reasoning, and latent reconstruction within a unified framework. MAS4TS first performs visual reasoning over time series plots with structured priors using a Vision-Language Model to extract temporal structures, and subsequently reconstructs predictive trajectories in latent space. Three specialized agents coordinate via shared memory and gated communication, while a router selects task-specific tool chains for execution. Extensive experiments on multiple benchmarks demonstrate that MAS4TS achieves state-of-the-art performance across a wide range of time series tasks, while exhibiting strong generalization and efficient inference.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Time series reasoning, one of 17 directions in LLM reasoning. Builds on 47 of its 72 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

47 of 72 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models15 Sep

Other references · 46

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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34 more
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  2. 2020Reformer: The Efficient Transformercited by 1,658
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
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  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
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  27. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  28. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 87
  29. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 59
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026TimeClaw: A Time-Series AI Agent with Exploratory Execution Learningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
  2. 2025TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
  3. 2026LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
  4. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
  5. 2025TempoGPT: Enhancing Time Series Reasoning via Quantizing Embedding
  6. 2026Harnessing Generalist Agents for Contextualized Time Series
  7. 2026Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning
  8. 2026TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation