Artificial intelligence offers powerful new tools for scientific discovery, but the interaction paradigms required to effectively harness these systems remain underexplored. In this paper, we present findings from a formative user study with 11 expert mathematicians who used AlphaEvolve, an evolutionary coding agent, to tackle advanced problems in their fields of expertise. We identify and characterize a distinct workflow we term intentmaking, the iterative process of discovering, defining, and refining one's experimental goals through active system interaction. We frame this as a natural extension to sensemaking, the cognitive process of building an understanding of complex or novel data. We suggest that users enter a cycle of intentmaking (defining and updating their experiment) and sensemaking (interpreting the results) which repeats many times during the course of an investigation. Our documentation of these themes suggests an approach to designing AI tools for scientific discovery that goes beyond the existing question/answer model of many current systems, treating them as collaborative instruments rather than opaque black-box assistants.

翻译：人工智能为科学发现提供了强大的新工具，但有效利用这些系统所需的交互范式仍尚未得到充分探索。本文报告了一项形成性用户研究的结果，涉及11位使用进化编码智能体AlphaEvolve处理其专业领域内高级问题的专家数学家。我们识别并描述了一种独特的工作流程，称之为“意图构建”，即通过主动与系统交互来发现、定义和完善实验目标的迭代过程。我们将其视为“意义建构”（理解复杂或新颖数据的认知过程）的自然扩展。我们提出，用户在调查过程中会进入一个迭代循环：意图构建（定义和更新实验）与意义建构（解释结果）多次重复。对这些主题的文档记录表明，设计用于科学发现的人工智能工具应采取一种方法，超越现有许多系统的问答模型，将其视为协作工具，而非不透明的黑箱助手。