We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.
@article{arxiv.2511.12829,
title = {An Evaluation of Representation Learning Methods in Particle Physics Foundation Models},
author = {Michael Chen and Raghav Kansal and Abhijith Gandrakota and Zichun Hao and Jennifer Ngadiuba and Maria Spiropulu},
journal= {arXiv preprint arXiv:2511.12829},
year = {2025}
}