mlsanityData to 14 Oct 2026

Foundation models for high-energy physics

arXiv 2509.21434 ↗First posted 25 Sep 2025hep-ph cs.AI cs.LGcited by 10

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Abstract (arXiv)

The rise of foundation models -- large, pretrained machine learning models that can be finetuned to a variety of tasks -- has revolutionized the fields of natural language processing and computer vision. In high-energy physics, the question of whether these models can be implemented directly in physics research, or even built from scratch, tailored for particle physics data, has generated an increasing amount of attention. This review, which is the first on the topic of foundation models in high-energy physics, summarizes and discusses the research that has been published in the field so far.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 52 references that are in the map. First posted on arXiv 25 Sep 2025.

Builds on

19 of 52 references.

References · 19

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,375
  3. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  4. 2022Particle Transformer for Jet Taggingcited by 98
  5. 2023Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networkscited by 82
  6. 2024Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Modelscited by 46
  7. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 43
  8. 2024A Lorentz-Equivariant Transformer for All of the LHCcited by 28
  9. 2020Jet tagging in the Lund plane with graph networkscited by 22
  10. 2024Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Modelscited by 22
  11. 2024Is Tokenization Needed for Masked Particle Modelling?cited by 22
  12. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
7 more
  1. 2024Finetuning Foundation Models for Joint Analysis Optimizationcited by 18
  2. 2024Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physicscited by 14
  3. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  4. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  5. 2024Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecturecited by 12
  6. 2024Bumblebee: Foundation Model for Particle Physics Discoverycited by 12
  7. 2024Pretrained Event Classification Model for High Energy Physics Analysiscited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

  1. 2026Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactionscited by 3
  2. 2026jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillationcited by 2
  3. 2026Towards Engineering Scaling Laws with Pretraining Data Compositioncited by 2
  4. 2026Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthroughcited by 2
  5. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 2
  6. 2026Machine-learned particle flow as a foundation model for collider physicscited by 1
  7. 2026Learning Standard Model structure from LHC data with Riemannian flow matchingcited by 0
  8. 2026Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHCcited by 0
  9. 2026Machine learning for the LHC physics program: a 2025-2026 stocktakecited by 0
  10. 2026Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discoverycited by 0