专家用AI解数学题时，发现需反复调整目标与理解结果，形成新协作模式。
Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery
- 通过与AI互动，逐步明确和修正研究目标，称为‘意图构建’。
- 数学家在探索中不断循环定义实验与解读结果，形成双轮驱动流程。
- 适合设计面向科研的协作型AI工具，突破问答式交互局限。
人工智能为科学发现提供了强大新工具，但有效利用这些系统的交互范式仍待探索。本文基于对11位资深数学家使用AlphaEvolve（一种进化式编码代理）解决其专业领域难题的初步用户研究，识别并刻画了一种独特的工作流程——‘意图构建’，即通过与系统持续互动，迭代发现、定义并优化实验目标。我们将这一过程视为‘意义建构’（sensemaking）的自然延伸，后者是理解复杂或新颖数据的认知过程。研究发现，用户在调查过程中会反复进入‘意图构建’（定义和更新实验）与‘意义建构’（解释结果）的循环。该发现表明，设计科学发现类AI工具应超越当前主流的问答模式，将其视为可协作的仪器，而非黑箱助手。
原文摘要 · Abstract (English)
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.
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