{
  "abstract": "Closed-loop neuromodulation requires sleep staging that respects stage-specific minimum dwells: stimulation policies switch by sleep stage, and a policy change is permitted only after the current stage has persisted for its minimum duration. Discriminative classifiers fragment stage trajectories---for example, a 30 s Wake segment inside a 10 min N2 run---and even hidden semi-Markov smoothing leaves 11.3% of segments below the minimum dwell threshold. This paper presents a world model in which a recurrent state-space model (RSSM) learns dwell structure endogenously from EEG, via duration-aware latent sampling, a survival-curve dwell prior, and transition-matrix calibration, and a trajectory-level hard-constraint layer guarantees the minimum dwell at decoding. On SHHS-1 (200 subjects, 160/20/20, three repeated splits), the model reaches a test accuracy of 0.690 +/- 0.053 (95% CI over 3 folds) versus 0.641 +/- 0.042 for an identically split discriminative baseline, with macro-F1 +17.5 pp and better accuracy on all three folds. On Sleep-EDF the constraint layer cuts below-threshold segments from 71.8% to 0.1% with no accuracy loss (0.734 to 0.737), and an online locked scheduler reduces RL policy switches by 87% per night while keeping every policy run at least 90 s. To the best of the author's knowledge, this is the first system to couple learned dwell statistics with a decoding-time hard guarantee. Code and pretrained weights will be released.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.33033",
  "author_total": 1,
  "authors": [
    {
      "name": "Junhui Gao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5qwj2sk1pv8hw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.33033",
  "fulltext_url": null,
  "id": "5qwj2sk1pv8hw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.33033",
  "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.NE"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.33033",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5qwj2sk1pv8hw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:44Z",
      "native_id": "2610.33033",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.33033"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Duration-Aware World Models for Sleep Staging with Dwell-Time Constraints",
  "title_text": "Duration-Aware World Models for Sleep Staging with Dwell-Time Constraints",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:44Z",
  "url": "https://research.draconitic.com/paper/5qwj2sk1pv8hw",
  "venue": "arXiv",
  "versions": []
}