多智能体框架让图表生成更准更快，还能越算越好。
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling
- 用多个专业智能体协作，分步完成图表设计与代码生成。
- 相比现有最佳方法提升5.2%准确率，计算量越大效果越佳。
- 分开处理视觉与文本反馈，显著增强模型自我纠错能力。
图表生成旨在生成代码以生成满足特定视觉属性（如文字、布局、颜色、类型）的图表，具有在金融分析、科研展示、教育和医疗等领域实现自动专业报告生成的巨大潜力。本文构建了一个基于视觉语言模型（VLM）的多智能体框架，用于高效自动图表生成。高质量图表生成需兼具强视觉设计能力和精确编码能力，以将期望的视觉属性嵌入代码中，这一复杂的多模态推理过程对直接提示VLM极具挑战。为此，我们提出METAL，一个将图表生成任务分解为多个专用智能体间迭代协作的多智能体框架。METAL在图表生成任务上较当前最佳结果提升5.2%。该框架表现出测试时缩放现象：随着计算预算从512增至8192个令牌，性能单调上升。此外，我们发现分离不同模态进行批判性评估可增强VLM在多模态情境下的自我修正能力。
原文摘要 · Abstract (English)
Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has great potential to empower the automatic professional report generation in financial analysis, research presentation, education, and healthcare. In this work, we build a vision-language model (VLM) based multi-agent framework for effective automatic chart generation. Generating high-quality charts requires both strong visual design skills and precise coding capabilities that embed the desired visual properties into code. Such a complex multi-modal reasoning process is difficult for direct prompting of VLMs. To resolve these challenges, we propose METAL, a multi-agent framework that decomposes the task of chart generation into the iterative collaboration among specialized agents. METAL achieves 5.2% improvement over the current best result in the chart generation task. The METAL framework exhibits the phenomenon of test-time scaling: its performance increases monotonically as the logarithmic computational budget grows from 512 to 8192 tokens. In addition, we find that separating different modalities during the critique process of METAL boosts the self-correction capability of VLMs in the multimodal context.
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