用多个AI代理自动完成数据探索到叙事生成全过程
Multi-Agent Data Visualization and Narrative Generation
- 设计轻量级多代理系统，分工协作处理数据分析全流程
- 在4个数据集上验证，生成叙事质量高且计算效率优
- 支持精准修改，适合需要持续迭代的人机协作场景
近年来，AI代理的发展改变了工作方式，实现了人与代理间更高效的自动化和协作。在数据可视化领域，多代理系统可贯穿数据到沟通的全链条。本文提出一种轻量级多代理系统，自动化完成从数据探索到生成连贯视觉叙事的分析流程。该方法结合混合多代理架构与确定性组件，将关键逻辑外置于大语言模型，提升透明度与可靠性。系统输出细粒度、模块化，支持局部修改而无需整体重生成，促进可持续的人机协作。我们在4个不同数据集上评估该系统，结果表明其具备强泛化能力、优质叙事表现及高效计算性能，依赖极少。
原文摘要 · Abstract (English)
Recent advancements in the field of AI agents have impacted the way we work, enabling greater automation and collaboration between humans and agents. In the data visualization field, multi-agent systems can be useful for employing agents throughout the entire data-to-communication pipeline. We present a lightweight multi-agent system that automates the data analysis workflow, from data exploration to generating coherent visual narratives for insight communication. Our approach combines a hybrid multi-agent architecture with deterministic components, strategically externalizing critical logic from LLMs to improve transparency and reliability. The system delivers granular, modular outputs that enable surgical modifications without full regeneration, supporting sustainable human-AI collaboration. We evaluated our system across 4 diverse datasets, demonstrating strong generalizability, narrative quality, and computational efficiency with minimal dependencies.
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