{
  "abstract": "Achieving global coordination without a central controller or dense global communication is a defining challenge for both biological collectives and engineered swarms. We introduce and analyze a minimal model in which self-propelled agents in a bounded domain periodically reorient their motions toward the centroid of a small, randomly chosen subset of their peers, with no direct sensing of any individual neighbor's position or heading. We show that this sparse, non-local sampling rule reliably drives an initially disordered population to a globally aligned, nematic state, and that shrinking the sampled subset -- down to the minimum of two agents -- accelerates ordering rather than impeding it: the resulting estimation noise, filtered through a geometric turning rule, is itself the engine of symmetry breaking. We derive an analytical criterion, in quantitative agreement with simulation, that predicts when this ordering succeeds as a function of the sampling size and sampling frequency. We confirm the mechanism experimentally in a swarm of up to fifteen differential-drive robots. These results identify sparse random sampling as an information-efficient route to collective coordination, with implications for understanding animal collectives and for designing communication-limited robotic swarms.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.10521",
  "author_total": 5,
  "authors": [
    {
      "name": "Sarath Sankar"
    },
    {
      "name": "Abhijit Sinha"
    },
    {
      "name": "Shankar Ghosh"
    },
    {
      "name": "Vikram Tripathi"
    },
    {
      "name": "Amitava Bhattacharya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a81yjbgmprxdw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.10521",
  "fulltext_url": null,
  "id": "a81yjbgmprxdw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.10521",
  "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.stat-mech"
    ],
    [
      "openalex",
      "1705|Distributed Control Multi-Agent Systems"
    ]
  ],
  "openalex": "W7221037327",
  "pdf_url": "https://arxiv.org/pdf/2610.10521",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a81yjbgmprxdw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.10521",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.10521"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Trend formation with sparse global sampling",
  "title_text": "Trend formation with sparse global sampling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/a81yjbgmprxdw",
  "venue": "arXiv",
  "versions": []
}