{"ID":2841139,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12829","arxiv_id":"2511.12829","title":"An Evaluation of Representation Learning Methods in Particle Physics Foundation Models","abstract":"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.","short_abstract":"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 compar...","url_abs":"https://arxiv.org/abs/2511.12829","url_pdf":"https://arxiv.org/pdf/2511.12829v1","authors":"[\"Michael Chen\",\"Raghav Kansal\",\"Abhijith Gandrakota\",\"Zichun Hao\",\"Jennifer Ngadiuba\",\"Maria Spiropulu\"]","published":"2025-11-16T23:23:47Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}