AI自动生成可视化应用，无需编程即可完成复杂数据分析任务。
Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
- 构建多智能体系统，自动规划、实现并验证可视化方案。
- 在IEEE SciVis竞赛中成功生成可运行的单页可视化应用。
- 适合科研人员快速构建定制化数据可视化工具，提升分析效率。
在几乎所有科学工作中，解读和传达复杂数据的能力至关重要，但通常需要超出核心领域之外的专业知识，涵盖数据管理、分析、可视化设计与实现。我们提出一种端到端的智能体框架，仅需数据和任务的高层次描述，即可自主设计定制化的可视化分析应用（VIS apps）。这标志着迈向通用人工智能科研合作者的重要一步：一个能基于高层指令自主执行长周期任务的系统。本文提出的可视化合作者是这一愿景的关键组件——通过多个智能体和专用技能协同工作，自主完成探索性分析、任务规划、环境配置、代码实现、界面验证，并关键地评估整体任务完成度。每个阶段生成文档与指令成果，指导后续迭代优化。我们在涵盖多个科学与工程领域的IEEE SciVis竞赛中验证了该方法的有效性。这些竞赛是理想的测试场，因其包含现实世界的复杂性：需求模糊、数据模态多样、设计权衡多、任务驱动验证。仅提供数据和目标任务，系统即可自主生成具备验证过的联动视图行为的功能性单页可视化应用，高度契合领域专家的任务需求。
原文摘要 · Abstract (English)
The ability to inspect, interpret, and communicate complex data is crucial for virtually any scientific endeavor, but often requires significant expertise outside the core domain ranging from data management and analysis to visualization design and implementation. We present an end-to-end agentic harness that, based on only the data and a high level description of the tasks, independently designs custom visual analysis applications (VIS apps). This represents an important step towards a general AI co-scientist envisioned by many as an autonomous system that can autonomously execute long horizon tasks based on high-level directions. Our proposed VIS co-scientist is an essential component of this broader AI co-scientist vision: a harness that can autonomously analyze data and design visualization solutions using a collection of agents and specialized skills that coordinate exploratory analysis, plan, configure the environment, implement, validate the interface, and most importantly evaluate the overall task completion. Each stage produces document and instruction artifacts that guide downstream work and enable iterative refinement. We validate this approach on IEEE SciVis Contests spanning multiple science and engineering fields. These contests serve as ideal proving grounds because they encode real-world complexity: ambiguous requirements, diverse data modalities, design trade-offs, and task-driven validation. Given only the data and target tasks, our system autonomously produces functional single-page VIS Apps with verified linked-view behavior, highly customized to domain experts' specified tasks and needs.
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