arXiv:2607. 23377v1 Announce Type: cross Abstract: The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent.
By Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
arXiv:2605. 29283v2 Announce Type: replace-cross Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed training distribution.
By Mengdi Chu, Yang Liu, Ayan Biswas, Han-Wei Shen
arXiv:2606. 19781v1 Announce Type: cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size.
By Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson, Benjamin Nachman
arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
By Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie
The paper investigates whether two particle physics foundation models, OmniLearned and ParticleViT, pretrained on high‑energy proton–proton and electron–proton collisions can transfer knowledge to a low‑energy neutrino experiment. Using MINERvA neutrino–nucleus scattering data, the authors evaluate the models on energy regression and charged‑current pion classification tasks, finding that the pretrained models outperform similarly sized models trained from scratch, with OmniLearned excelling in regression and ParticleViT in classification. When the same transformer architecture is initialized from unrelated text pretraining (BERT), the performance advantage is minimal for classification and absent for regression, indicating that particle‑level foundation models capture inductive biases that generalize across energy scales, detector technologies, and physics processes.
By Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman, Callum Wilkinson
arXiv:2607. 27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scientific datasets, using a fully connected autoencoder model with approximately 3 million parameters.
By Liangyu Wu, Qibin Liu, Alexander Yue, Julia Gonski