[2607.09211] Co-evolution of self-replication and function in a digital primordial soup

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Computer Science > Neural and Evolutionary Computing

arXiv:2607.09211 (cs)

[Submitted on 10 Jul 2026]

Title:Co-evolution of self-replication and function in a digital primordial soup

Authors:Francesco Cicala, Eyvind Niklasson, Ettore Randazzo, Sami Boukortt, Alessio Basti, Mayalen Etcheverry, Rif A. Saurous, Ben Laurie, James Manyika, Blaise Aguera-Arcas, Blake Richards<br>View a PDF of the paper titled Co-evolution of self-replication and function in a digital primordial soup, by Francesco Cicala and 10 other authors

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Abstract:While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the co-evolution of this emergent self-replication alongside problem-solving capabilities. We initialize a population of random 32-byte Z80 assembly programs, requiring self-replication to arise purely through random assembly-level mutations and pairwise program interactions. To link these behaviors, we introduce a task-based validation step: correctly evaluating a polynomial raises a program's interaction probability above a baseline rate. Our experiments yield four primary findings. First, self-replication and mathematical problem-solving successfully co-evolve from initial randomness. Second, the pressure to compute accelerates the emergence of compact, robust reproductive architectures that preserve memory for task execution. Third, applying metabolic constraints increases the likelihood that programs evolve conditional halting, terminating early during validation while bypassing the halt during interaction to execute block-copy replication. Finally, when programs are partitioned into spatial task niches, spontaneous self-replication generates an emergent learning curriculum, utilizing simple solutions as stepping stones toward complex polynomials. Altogether, these results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.

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Neural and Evolutionary Computing (cs.NE)

Cite as:<br>arXiv:2607.09211 [cs.NE]

(or<br>arXiv:2607.09211v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2607.09211

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Francesco Cicala [view email]<br>[v1]<br>Fri, 10 Jul 2026 08:59:25 UTC (8,873 KB)

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