Scaling Collider Event Generation with Residual-Quantized Tokens
hep-ex, cs.LG, hep-ph, physics.data-an
Submitted: 2026-09-30
Updated: 2026-09-30
Sources
- Machine Learning and LHC Event Generation
- CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
- OmniJet-$\alpha$: The first cross-task foundation model for particle physics
- Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
- Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
- OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
- OmniJet-${\alpha}_C$: Learning point cloud calorimeter simulations using generative transformers
- Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning
- Neural Scaling Laws for Boosted Jet Tagging
- Neural Scaling Laws for Jet Generation
- Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
- Neural Discrete Representation Learning
- SoundStream: An End-to-End Neural Audio Codec
- Attention Is All You Need
- GLU Variants Improve Transformer
- Energy Correlation Functions for Jet Substructure
- Power Counting to Better Jet Observables
- Training Compute-Optimal Large Language Models
- Skaling: Chinchilla's Exponents Meet Kaplan's Coupling
- Prescriptive Scaling Laws for Data Constrained Training