{"slug": "co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup", "title": "Co-evolution of self-replication and function in a digital primordial soup", "summary": "Researchers at an undisclosed institution demonstrated that self-replication and mathematical problem-solving can co-evolve from random assembly code in a digital primordial soup. Using Z80 assembly programs, they found that task demands shape reproductive architectures and that spatial niches create an emergent learning curriculum. The work highlights an interactive feedback loop between environmental tasks and evolutionary dynamics.", "body_md": "# Computer Science > Neural and Evolutionary Computing\n\n[Submitted on 10 Jul 2026]\n\n# Title:Co-evolution of self-replication and function in a digital primordial soup\n\n[View PDF](/pdf/2607.09211)\n\n[HTML (experimental)](https://arxiv.org/html/2607.09211v1)\n\nAbstract: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.\n\n## Submission history\n\nFrom: Francesco Cicala [[view email](/show-email/22722b63/2607.09211)]\n\n**[v1]** Fri, 10 Jul 2026 08:59:25 UTC (8,873 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup", "canonical_source": "https://arxiv.org/abs/2607.09211", "published_at": "2026-07-18 21:09:01+00:00", "updated_at": "2026-07-18 23:02:41.754861+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning"], "entities": ["Francesco Cicala"], "alternates": {"html": "https://wpnews.pro/news/co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup", "markdown": "https://wpnews.pro/news/co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup.md", "text": "https://wpnews.pro/news/co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup.txt", "jsonld": "https://wpnews.pro/news/co-evolution-of-self-replication-and-function-in-a-digital-primordial-soup.jsonld"}}