Scaling Collider Event Generation with Residual-Quantized Tokens
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based surrogates. At the same time, LLMs have driven fast progress in generative discrete modeling: autoregressive transformers trained on tokenized data now represent the state of the art across a range of generative tasks. We extend the discrete modeling paradigm by introducing a particle-level generative model trained on residual-quantized full-event data. We demonstrate the ability of this model family to perform conditional generation from detector-stable particles; we study its scaling behavior a...
| Authors | Dan Godi, Dmitrii Kobylianskii, Eilam Gross |
|---|---|
| Primary category | hep-ex |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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