{
  "abstract": "We introduce structure-aware optimal transport diagnostics for assessing whether empirical time-series dynamics are compatible with a prescribed predictable-drift class. Our approach translates a pathwise property of stochastic processes into a finite-dimensional projection problem over empirical transition laws, encoding admissibility as row-wise moment constraints on local couplings and quantifying deviations via a Kullback--Leibler projection gap $G_{λ,k}$. This yields both a principled diagnostic and a scalable Bregman--Dykstra algorithm that retains the efficiency of Sinkhorn-type methods while incorporating structural constraints. Calibrated against a Brownian reference null, $G_{λ,k}$ separates process classes that are indistinguishable by second-order statistics alone---including fractional Brownian motion and BSS processes sharing identical Hurst indices---without asymptotic resampling or continuous-time recovery. This positions the method as a general tool for structure-aware model validation of sequential data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08505",
  "author_total": 4,
  "authors": [
    {
      "name": "Abigail Langbridge"
    },
    {
      "name": "Enrico Biffis"
    },
    {
      "name": "Jakub Mareček"
    },
    {
      "name": "Robert Shorten"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ag12npkrk11x1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08505",
  "fulltext_url": null,
  "id": "ag12npkrk11x1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08505",
  "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",
      "math.OC"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08505",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ag12npkrk11x1.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:07Z",
      "native_id": "2611.08505",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08505"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Drift-admissibility via Optimal Transport",
  "title_text": "Drift-admissibility via Optimal Transport",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:13Z",
  "url": "https://research.draconitic.com/paper/ag12npkrk11x1",
  "venue": "arXiv",
  "versions": []
}