用多智能体系统让模型看懂时间序列并自适应推理
Visual Reasoning over Time Series via Multi-Agent System
- 设计多智能体协作框架，通过视觉理解时间序列图结构
- 在多个基准上达到顶尖性能，支持跨任务泛化
- 适合需要动态工具选择的复杂时间序列分析场景
时间序列分析广泛应用于现实世界，但现有方法在整合直观视觉推理和跨任务泛化能力方面仍受限。为此，我们提出MAS4TS，一种基于分析-推理-执行范式的工具驱动型多智能体系统，统一集成智能体通信、视觉推理与潜在空间重构。MAS4TS首先利用视觉语言模型对时间序列图进行带结构先验的视觉推理，提取时序结构，并在潜在空间中重建预测轨迹。三个专用智能体通过共享内存与门控通信协同工作，路由模块则根据任务选择特定工具链执行。在多个基准上的大量实验表明，MAS4TS在广泛的时间序列任务中达到最先进性能，具备强泛化能力与高效推理特性。
原文摘要 · Abstract (English)
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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