Speaker
Description
Foundation models have demonstrated remarkable performance in collider physics, yet little is understood about the geometric principles underlying their neural representations. We propose representation geometry as a new direction for Scientific AI, where machine learning models serve not only as powerful predictors but also as “microscopes” for revealing the intrinsic structure of the underlying physical data. In this work, we study the OmniJet family of collider foundation models pre-trained with three different self-supervised objectives on the JetClass benchmark dataset. Using the Gromov-Wasserstein distance, we compare the metric geometry of representation spaces across network blocks, training steps, and pre-training schemes. Our preliminary results uncover highly structured geometric evolution both across network depth and throughout training, while suggesting that learned representations exhibit both common structural features and systematic variations induced by pre-training objectives. These studies establish a framework for investigating whether and to what extent collider foundation models converge toward common geometric structures, laying the groundwork for future empirical tests of the Platonic Representation Hypothesis in a scientific domain whose underlying reality is governed by fundamental physical laws.