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原文摘要

Artificial intelligence offers powerful paradigms for scientific discovery, orchestrating autonomous inner search loops over long horizons. However, discovery remains bottlenecked by the friction of the outer human-AI loop: the cycle where domain experts formulate research objectives, diagnose reward hacks, and extract meaning. We present findings from a study with 11 expert mathematicians using AlphaEvolve, an evolutionary coding agent, across more than 2,300 experiments over three months. We identify and characterize intentmaking, the iterative process of discovering, defining, and refining experimental goals through system interaction, as a reciprocal counterpart to sensemaking. We observe that domain experts effectively alternate between steering the search space (intentmaking) and extracting mathematical insights (sensemaking). Our findings suggest an approach to designing AI tools for scientific discovery that moves beyond the existing question--answer model, treating them as collaborative instruments rather than opaque black-box oracles.