{
  "abstract": "Classical CUSUM relies on the log-likelihood ratio of the underlying distributions, which cannot generally be computed from finite pre- and post-change samples alone. We propose diffusion-integrated score CUSUM (DI-SCUSUM), a training-free detector. We add Gaussian noise to the samples to form two smooth density estimates and calculate their Hyvärinen scores exactly, without training a score network. For each incoming observation, we sample a diffusion time, perturb the observation, and use the importance-weighted score difference as an increment in the DI-SCUSUM recursion. Under the assumption that observations follow the fixed empirical distributions, the post-change mean increment is proportional to the Kullback-Leibler (KL) divergence from the smoothed post-change to the smoothed pre-change empirical distribution. We establish exponential false-alarm scaling and a first-order delay bound that, for a fixed threshold and increment scaling, is inversely proportional to the KL divergence. In the calibrated anisotropic Gaussian simulation, DI-SCUSUM nearly matches likelihood-ratio CUSUM and reduces the measured detection delay by about 91% relative to score-based CUSUM. On MNIST and Oxford-IIIT Pet, DI-SCUSUM also has lower empirical conditional detection delay than SCUSUM at comparable false-alarm levels.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12200",
  "author_total": 4,
  "authors": [
    {
      "name": "Arman Adibi"
    },
    {
      "name": "Mohammadreza Maleki"
    },
    {
      "name": "Sanjeev Kulkarni"
    },
    {
      "name": "H. Vincent Poor"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2amzj6sqay330.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12200",
  "fulltext_url": null,
  "id": "2amzj6sqay330",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12200",
  "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",
      "stat.ML"
    ],
    [
      "openalex",
      "1804|Advanced Statistical Process Monitoring"
    ]
  ],
  "openalex": "W7221179236",
  "pdf_url": "https://arxiv.org/pdf/2610.12200",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2amzj6sqay330.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:03Z",
      "native_id": "2610.12200",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12200"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Quickest Change Detection with Diffusion-Integrated Scores",
  "title_text": "Quickest Change Detection with Diffusion-Integrated Scores",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:03Z",
  "url": "https://research.draconitic.com/paper/2amzj6sqay330",
  "venue": "arXiv",
  "versions": []
}