标题： 以评估为中心的科学可视化代理范式 显示英文标题

标题： An Evaluation-Centric Paradigm for Scientific Visualization Agents

摘要： 近年来，多模态大语言模型（MLLMs）的进展使得越来越复杂的自主可视化代理成为可能，这些代理能够将用户意图转化为数据可视化。 然而，由于缺乏全面的大规模基准来评估实际能力，衡量进展和比较不同代理仍然具有挑战性，尤其是在科学可视化（SciVis）领域。 本文档探讨了SciVis代理所需的各种评估类型，概述了相关挑战，提供了一个简单的概念验证评估示例，并讨论了评估基准如何促进代理的自我改进。 我们倡导更广泛的合作，以开发一个SciVis代理评估基准，这不仅能够评估现有能力，还能推动该领域的创新并激发未来的发展。 显示英文摘要

摘要： Recent advances in multi-modal large language models (MLLMs) have enabled increasingly sophisticated autonomous visualization agents capable of translating user intentions into data visualizations. However, measuring progress and comparing different agents remains challenging, particularly in scientific visualization (SciVis), due to the absence of comprehensive, large-scale benchmarks for evaluating real-world capabilities. This position paper examines the various types of evaluation required for SciVis agents, outlines the associated challenges, provides a simple proof-of-concept evaluation example, and discusses how evaluation benchmarks can facilitate agent self-improvement. We advocate for a broader collaboration to develop a SciVis agentic evaluation benchmark that would not only assess existing capabilities but also drive innovation and stimulate future development in the field.