发表机构
California Institute of Technology（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将数据解释作为大型语言模型处理时空场的阶段，以物理不变量为数据表示，在PDE发现基准中使方程恢复准确率近三倍提升，为自动化场理论构建提供实用途径。
AI 中文摘要
理解分子相互作用如何控制宏观行为是分子科学的核心挑战，但传统理论构建无法跟上现代实验产生的海量数据集。大型语言模型为自动化理论构建提供了有前景的途径，但时空场无法直接放入提示词中，现有模型通常仅通过衡量每个提议与数据拟合程度的分数来学习数据。本文引入数据解释阶段，该阶段将场测量为理论学家会参考的量，并将其作为直接输入提供给模型。在模拟场的基准测试中，与展示原始数据相比，数据解释使恢复方程的准确性几乎提高了两倍，且计算成本可忽略不计，无需任何训练。通过让语言模型像理论学家一样读取场数据，数据解释为自动化场理论构建提供了实用途径，可与实验协同发展。
英文摘要
Understanding how molecular interactions govern macroscopic behaviour is a central challenge in molecular sciences. However, conventional theory building cannot keep pace with the vast datasets modern experimentation routinely produces. Large language models offer a promising route to automating theory construction, but a spatiotemporal field cannot be directly placed in a prompt. Existing models generally learn about the data only through a score measuring how well each proposal fits it. Here we introduce data interpretation, a stage that measures the field into the quantities a theorist would consult and supplies them to the model as a direct input. On a benchmark of simulated fields, interpretation nearly triples the accuracy of recovered equations relative to showing the raw data, at negligible computational cost and without any training. By allowing a language model to read field data as a theorist does, data interpretation offers a practical route to automated field theory construction that can coevolve with experimentation.
Comments6 pages, 1 figure
