SOURCE-LINKED INTELLIGENCE
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 mec
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-02T16:05:40.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.