{
  "abstract": "We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the Berezinskii-Kosterlitz-Thouless transition. The architecture is combined with physics-informed ingredients: locality-informed attention and approximate probabilities. The resulting sampler significantly outperforms existing neural samplers. We train the model on lattices up to $96\\times96$, verify with a mode-dropping estimator that no parts of the configuration space are missed, and reproduce the energy, magnetization and helicity modulus obtained with the traditional Markov Chain Monte Carlo algorithm.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23783",
  "author_total": 4,
  "authors": [
    {
      "name": "Piotr Białas"
    },
    {
      "name": "Christian D. Morales"
    },
    {
      "name": "Tomasz Stebel"
    },
    {
      "name": "Dawid Zapolski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/33thb6yynjf37.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23783",
  "fulltext_url": null,
  "id": "33thb6yynjf37",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23783",
  "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",
      "cond-mat.dis-nn"
    ],
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "hep-lat"
    ],
    [
      "openalex",
      "3104|Theoretical and Computational Physics"
    ]
  ],
  "openalex": "W7222961408",
  "pdf_url": "https://arxiv.org/pdf/2610.23783",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/33thb6yynjf37.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2610.23783",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23783"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.dis-nn",
      "subfield": "condensed-matter"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-lat",
      "subfield": "high-energy"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Sampling the XY model with an attention-based neural network",
  "title_text": "Sampling the XY model with an attention-based neural network",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/33thb6yynjf37",
  "venue": "arXiv",
  "versions": []
}