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Tabular foundation models show physics interpolation, not understanding

By PulseAugur Editorial · [1 sources] ·

A new paper investigates whether tabular foundation models (TFMs) have learned physics principles from the data they are trained on. Researchers evaluated four TFMs, including TabPFN-3 and Real-TabPFN-2.5, against six baselines using datasets derived from 316 physical equations. The study found that TFMs significantly outperform baselines, but they fail to represent noiseless mechanisms or physical units, indicating they interpolate physics without truly acting as physical models. AI

IMPACT This research highlights limitations in current tabular foundation models' ability to truly understand physical principles, suggesting a need for improved architectures or training methodologies.

RANK_REASON The cluster contains an academic paper detailing research findings on AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Tabular foundation models show physics interpolation, not understanding

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The cluster contains an academic paper detailing research findings on AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur, Pierre-Luc Bacon ·

    Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit

    arXiv:2609.02766v1 Announce Type: new Abstract: 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 Bayesi…

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