{
  "abstract": "Foundation models often obtain powerful results through very large unlabeled training datasets that dwarf available labeled datasets in size, but high energy physics already provides large samples of high-fidelity labeled simulation. We therefore ask whether self-supervised objectives improve over supervised pretraining alone when large labeled samples already exist. We introduce SuperJEPA, a joint-embedding predictive architec- ture augmented with a supervised objective, and find improved downstream jet-tagging performance across multiple tasks, model sizes, and pretraining dataset sizes. The gains persist for both frozen and fine-tuned encoders, motivating predictive self-supervision in future large-scale particle-physics foundation models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.36121",
  "author_total": 2,
  "authors": [
    {
      "name": "Dylan Sheldon Rankin"
    },
    {
      "name": "Ho Fung Tsoi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d7610mzjcq605.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.36121",
  "fulltext_url": null,
  "id": "d7610mzjcq605",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.36121",
  "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"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7224107630",
  "pdf_url": "https://arxiv.org/pdf/2610.36121",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d7610mzjcq605.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:50Z",
      "native_id": "2610.36121",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.36121"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "SuperJEPA: Pretraining with a Supervision-Augmented Joint-Embedding Predictive Architecture for High Energy Physics",
  "title_text": "SuperJEPA: Pretraining with a Supervision-Augmented Joint-Embedding Predictive Architecture for High Energy Physics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:50Z",
  "url": "https://research.draconitic.com/paper/d7610mzjcq605",
  "venue": "arXiv",
  "versions": []
}