Visual Reasoning over Time Series via Multi-Agent System

2026-02-03

AI Summary

This work addresses the limitations of existing time series methods, which lack visual reasoning capabilities and struggle with cross-task generalization and adaptive tool utilization. To overcome these challenges, the authors propose MAS4TS, the first framework that unifies multi-agent collaboration, vision-language model–driven structured visual reasoning, and dynamic toolchain selection within a time series analysis pipeline. Built upon an Analyzer-Reasoner-Executor paradigm, MAS4TS enables efficient agent coordination through latent trajectory reconstruction, shared memory, and gated communication mechanisms. Experimental results demonstrate that MAS4TS achieves state-of-the-art performance across multiple benchmarks, significantly enhancing both cross-task generalization and reasoning efficiency.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Multiagent Systems: Coordination and Collaboration
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• time series analysis
• visual reasoning
• task generalization
• adaptive tool usage
• multi-agent system

Innovation

• multi-agent system
• visual reasoning
• time series analysis
• tool-augmented reasoning
• latent reconstruction