Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
Source: https://arxiv.org/abs/2605.21825
PDF: https://arxiv.org/pdf/2605.21825v1
Content fetched: 2026-08-08T04:19:11.303620+00:00
Grounding: opencli arxiv paper metadata/abstract (arxiv_id=2605.21825)
Metadata
- Authors: Haichao Miao, Zhimin Li, Kuangshi Ai, Kaiyuan Tang, Chaoli Wang, Peer-Timo Bremer, Shusen Liu
- Published: 2026-05-20
- Primary category: cs.AI
- Categories: cs.AI, cs.HC
- AAIF quality score: 4
中文摘要
论文提出一个端到端 VIS co-scientist agent harness：只给数据和高层任务描述，就由多 agent 与专门技能协作完成探索分析计划环境配置实现验证和总体任务评估验证场景来自 IEEE SciVis Contests，强调真实科学可视化任务中的模糊需求多模态数据设计取舍和任务驱动验证
English Summary
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....
Abstract
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.