Co-evolution of self-replication and function in a digital primordial soup
vicgalle_
54 points
7 comments
July 18, 2026
Related Discussions
Found 5 related stories in 93.7ms across 9,716 title embeddings via pgvector HNSW
- Dream-RSI: Recursive Self-Improvement through Evolving Worlds bananaflag · 191 pts · September 16, 2026 · 51% similar
- Recursive self-improvement through collective intelligence hardmaru · 93 pts · October 11, 2026 · 48% similar
- AI researchers debate how close we are to recursive self-improvement artninja1988 · 85 pts · September 11, 2026 · 46% similar
- AI models may be capable of self-replicating without access to a datacenter artninja1988 · 83 pts · October 12, 2026 · 45% similar
- It may soon be possible to create "mirror life" amai · 58 pts · October 07, 2026 · 44% similar
Discussion Highlights (5 comments)
vicgalle_
An independent reproduction of the main result: https://github.com/vicgalle/coevolution-soup
HarHarVeryFunny
This reminds me of multi-head neural nets where there is synergy from having to learn two or more tasks at the same time that helps them all.
ericbarrett
This is a cool finding; I did not know it was still an active area of study with all the work on ML and LLMs these days. I have done some amateur exploration of the space and the result does not surprise me: https://github.com/ehbar/evol
EvanAnderson
Tierra[0], written by Tom Ray[1], immediately comes to mind. I was captivated when I read about it, as a teenager, in Steven Levy's "Artificial Life"[2]. Having played Core War[3], the description of Tierra in Levy's book inspired me to play around with making a virtual machine in Turbo Pascal and trying my hand at making a pale and naive clone. It was a lot of fun, and arguably has influenced a lot of my thinking about the origin of biological life. [0] https://tomray.me/tierra/whatis.html [1] https://en.wikipedia.org/wiki/Thomas_S._Ray [2] https://www.stevenlevy.com/artificial-life [3] https://en.wikipedia.org/wiki/Core_War
vatsachak
Interesting. But, evolution is way too unconstrained to provide us a path to "agi". It would require too much compute. Evolution also eventually gets frustrated and creates the brain, capable of in context learning. Maybe we should take some notes from these massively parallel, shallow, and highly recurrent constructions.