Visual Reasoning over Time Series via Multi-Agent System

• URL: http://arxiv.org/abs/2602.03026v1
• Date: Tue, 03 Feb 2026 02:48:57 GMT
• Title: Visual Reasoning over Time Series via Multi-Agent System
• Authors: Weilin Ruan, Yuxuan Liang,
• Abstract summary: MAS4TS is a tool-driven multi-agent system for general time series tasks.<n>It integrates agent communication, visual reasoning, and latent reconstruction within a unified framework.<n>It achieves state-of-the-art performance across a wide range of time series tasks, while exhibiting strong generalization and efficient inference.
• Score: 36.948425602257295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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