Computer Science > Neural and Evolutionary Computing

[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

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

View PDF HTML (experimental)

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.

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

arXiv-issued DOI via DataCite (pending registration)

Subjects:	Neural and Evolutionary Computing (cs.NE)
Cite as:	arXiv:2607.09211 [cs.NE]
	(or arXiv:2607.09211v1 [cs.NE] for this version)

Submission history

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

Full-text links:

Access Paper:

• 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

• View PDF

• HTML (experimental)

• TeX Source

view license

Current browse context:

cs.NE

< prev | next >

new | recent | 2026-07

Change to browse by:

cs

References & Citations

• NASA ADS

• Google Scholar

• Semantic Scholar

Loading...

BibTeX formatted citation

Data provided by:

Bookmark

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What is Litmaps?)

scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)

Hugging Face Spaces (What is Spaces?)

TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

• Author

• Venue

• Institution

• Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)