{
  "abstract": "Collective behaviour in living systems is usually modelled as the outcome of a \\emph{direct} social drive: agents are rewarded, or hard-wired, to align with or approach their neighbours. Here we show that aggregation can instead emerge from an \\emph{indirect} objective. We let reinforcement learning foragers, initially performing a random walk, optimize their dynamics from a purely individual reward for finding replenishable targets, while perceiving only their conspecifics and never the targets themselves. As the visual range grows, the agents undergo a sharp crossover from an environment-tuned individual search to a scale-agnostic collective one, and this crossover coincides with the onset of spatial aggregation. Thus a collective phase arises as a by-product of optimal foraging, without any direct reward for grouping. A minimal analytical first-passage model reproduces the transition as a crossover between the two search strategies. Our results identify indirect, resource-driven reward as a generic route to emergent collective phenomena.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.28046",
  "author_total": 6,
  "authors": [
    {
      "name": "Gorka Muñoz-Gil"
    },
    {
      "name": "Andrea López-Incera"
    },
    {
      "name": "Vide Ramsten"
    },
    {
      "name": "Giovanni Volpe"
    },
    {
      "name": "Thomas Müller"
    },
    {
      "name": "Hans J. Briegel"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/684r5g4pyvq1t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.28046",
  "fulltext_url": null,
  "id": "684r5g4pyvq1t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.28046",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "nlin.AO"
    ],
    [
      "arxiv",
      "physics.bio-ph"
    ],
    [
      "openalex",
      "1705|Distributed Control Multi-Agent Systems"
    ]
  ],
  "openalex": "W7204777618",
  "pdf_url": "https://arxiv.org/pdf/2608.28046",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/684r5g4pyvq1t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "2608.28046",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.28046"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.bio-ph",
      "subfield": "biological-medical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Emergent aggregation from collective foraging",
  "title_text": "Emergent aggregation from collective foraging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/684r5g4pyvq1t",
  "venue": "arXiv",
  "versions": []
}