{
  "abstract": "Arguments that AI systems will evolve in harmful ways are largely made in the abstract. We provide a concrete empirical example. We simulate simple markets consisting of populations of twenty Large Language Model agents tasked with solving arithmetic problems. Agents differ only in their system prompt; those that go bankrupt are removed and replaced by mutated copies of the prompts of successful agents. Two market conditions select for opposite phenotypes: a market that rewards accuracy produces agents with high accuracy (94%) and high token use; a market that charges per token produces agents with low accuracy (10%) and low token use. In the second market, only tokens are incentivised yet we see a collapse in accuracy. Token use and accuracy are correlated traits such that selection on one also moves the other, and behaviour adapts along an unpriced dimension. A third neutral condition with no selection allows us to separate these effects from bias in the mutation operator. Adaptation occurs through a collective optimisation process that operates above the level of individual agents, who are never told the payment rule, do not receive feedback, and have no memory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.24033",
  "author_total": 4,
  "authors": [
    {
      "name": "Charlie Pilgrim"
    },
    {
      "name": "Christopher Krupenye"
    },
    {
      "name": "Dora Biro"
    },
    {
      "name": "Richard P. Mann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/08g6pb78gf8c9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.24033",
  "fulltext_url": null,
  "id": "08g6pb78gf8c9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.24033",
  "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.AI"
    ],
    [
      "arxiv",
      "cs.CL"
    ],
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "q-bio.PE"
    ],
    [
      "openalex",
      "1702|Evolutionary Algorithms and Applications"
    ]
  ],
  "openalex": "W7223005437",
  "pdf_url": "https://arxiv.org/pdf/2610.24033",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/08g6pb78gf8c9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2610.24033",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.24033"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.PE",
      "subfield": "evolutionary-biology"
    }
  ],
  "title": "LLM Evolution in a Simple Simulated Market",
  "title_text": "LLM Evolution in a Simple Simulated Market",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/08g6pb78gf8c9",
  "venue": "arXiv",
  "versions": []
}