Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks

arXiv:2609.31862 · hep-ph, cs.LG, hep-ex, physics.data-an · Submitted 2026-09-25 · Read on arXiv

hep-ph, cs.LG, hep-ex, physics.data-an

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/FM-for-HEP/hep-any2any

Terminology

• AUC — 30× in this paper · explained in 2 episodes
• Transformer — 28× in this paper · explained in 2 episodes
• Foundation Model — 23× in this paper · explained in 3 episodes
• Foundation Models — 15× in this paper · explained in 7 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Generative Models — 4× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• International Conference on Machine Learning — 2× in this paper
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Large Hadron Collider — 1× in this paper
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Simulation-based Inference — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• US Department of Energy — 1× in this paper

Sources

• End-to-end event reconstruction for precision physics at future colliders
• BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
• Foundation models for high-energy physics
• Bumblebee: Foundation Model for Particle Physics Discovery
• Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
• HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture
• Accelerating Resonance Searches via Signature-Oriented Pre-training
• AION-1: Omnimodal Foundation Model for Astronomical Sciences
• PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
• Machine-learned particle flow as a foundation model for collider physics
• Neural Scaling Laws for Jet Generation
• Towards Engineering Scaling Laws with Pretraining Data Composition
• Scaling laws for amplitude surrogates
• On the Opportunities and Risks of Foundation Models
• ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset
• NWT: Towards natural audio-to-video generation with representation learning
• Gaussian Error Linear Units (GELUs)
• Fast Transformer Decoding: One Write-Head is All You Need
• General Frameworks for Conditional Two-Sample Testing

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