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arXiv · 2610.24033

LLM Evolution in a Simple Simulated Market

Charlie Pilgrim·Christopher Krupenye·Dora Biro·Richard P. Mann
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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.

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Charlie Pilgrim, Christopher Krupenye, Dora Biro, Richard P. Mann. 2026-10-16. LLM Evolution in a Simple Simulated Market. https://arxiv.org/abs/2610.24033

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