{
  "abstract": "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 across a range of dataset and model sizes, characterize the effects of repeated data exposure and demonstrate that token-level loss systematically predicts downstream physical fidelity. These results provide an empirical framework for scalable collider full-event generation based on residual-quantized representations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00569",
  "author_total": 3,
  "authors": [
    {
      "name": "Dan Godi"
    },
    {
      "name": "Dmitrii Kobylianskii"
    },
    {
      "name": "Eilam Gross"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/09p2mv0wq6e7c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00569",
  "fulltext_url": null,
  "id": "09p2mv0wq6e7c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00569",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "arxiv",
      "physics.data-an"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7216424133",
  "pdf_url": "https://arxiv.org/pdf/2610.00569",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/09p2mv0wq6e7c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.00569",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00569"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Scaling Collider Event Generation with Residual-Quantized Tokens",
  "title_text": "Scaling Collider Event Generation with Residual-Quantized Tokens",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/09p2mv0wq6e7c",
  "venue": "arXiv",
  "versions": []
}