arXiv Machine Learning By Michelangelo Domina, Michele Ceriotti

Using large language models to probe the limits of atom-centered structural descriptors

Read the original on arXiv Machine Learning →

arXiv:2607. 26984v1 Announce Type: cross Abstract: Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling.

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arXiv Machine Learning
Sep 24

What Converges in the Platonic Representation Hypothesis? Structure over Geometry

The paper investigates the Platonic Representation Hypothesis, which posits that more capable models converge toward shared representations. By distinguishing relational structure (which samples are related) from metric geometry (quantitative relations like distances), the authors develop a controlled $2 imes2$ framework to evaluate both aspects at local and global scales. Their findings show that relational structure consistently converges across vision‑language and video‑text models, while metric geometry converges much more weakly, a pattern that persists even when using a Riemannian metric approximation.

By Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung