让多个AI智能体协作生成可视化图表，提升复杂数据处理能力
CoDA: Agentic Systems for Collaborative Data Visualization
- 设计四个专业AI智能体分工协作：分析元数据、规划任务、生成代码、自我反思
- 在复杂多文件数据上表现更优，综合评分比现有方法最高提升41.5%
- 适合需要自动化处理复杂数据的科研与数据分析人员
深度学习已革新数据分析，但数据科学家仍需大量时间手动制作可视化图表，亟需从自然语言查询实现强健的自动化。然而，当前系统在包含多文件的复杂数据集和迭代优化方面表现不足。现有方法，包括单智能体或多智能体系统，常简化任务，侧重初始查询解析，却难以应对数据复杂性、代码错误或最终可视化质量。本文将该挑战重新定义为协作式多智能体问题。我们提出CoDA，一个采用专门LLM智能体进行元数据分析、任务规划、代码生成与自我反思的多智能体系统。我们形式化该流程，证明基于元数据的分析可规避令牌限制，质量驱动的优化确保鲁棒性。大量评估显示，CoDA在综合得分上取得显著提升，优于竞争基线最多达41.5%。本工作表明，可视化自动化的未来不在于孤立的代码生成，而在于集成的协作式智能体工作流。
原文摘要 · Abstract (English)
Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation from natural language queries. However, current systems struggle with complex datasets containing multiple files and iterative refinement. Existing approaches, including simple single- or multi-agent systems, often oversimplify the task, focusing on initial query parsing while failing to robustly manage data complexity, code errors, or final visualization quality. In this paper, we reframe this challenge as a collaborative multi-agent problem. We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection. We formalize this pipeline, demonstrating how metadata-focused analysis bypasses token limits and quality-driven refinement ensures robustness. Extensive evaluations show CoDA achieves substantial gains in the overall score, outperforming competitive baselines by up to 41.5%. This work demonstrates that the future of visualization automation lies not in isolated code generation but in integrated, collaborative agentic workflows.
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