这篇论文测试了4个表格模型在316个物理方程上的表现，发现它们能插值物理但不懂单位和噪声机制。
研究评估了四个表格基础模型(TabPFN-3、TabICLv2、TabDPT和Real-TabPFN-2.5)在316个物理方程数据集上的表现。这些模型在基准测试中表现优于六种基线方法，无需微调即可主导结果。然而研究表明，这些模型的先验知识既无法表示无噪声机制，也无法处理物理单位，因此它们只能插值物理数据，尚不能作为真正的物理模型使用。
Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
Tabular foundation models (TFMs) learn to fill in tables the way language models fill in text, and tables are arguably the format in which most physical measurement arrives. Did they learn any physics in the process? They are Bayesian by construction, so the question is what their prior contains. We probe it directly, evaluating four of them (TabPFN-3, TabICLv2, TabDPT and Real-TabPFN-2.5) against six baselines on datasets sampled from 316 physical equations, in and out of domain. TFMs dominate, out of the box and after tuning. But we show that their prior can represent neither a noiseless mechanism nor physical units, which is why they interpolate physics without yet being able to act as physical models.