It doesn't mention it in the article, but guessing this is based on / inspired by AlphaEvolve?

Though I'm not sure the public can access AlphaEvolve yet.

(https://arxiv.org/abs/2506.13131)

If AlphaEvolve is: "a quality-diversity search framework for algorithm discovery" then maybe.

At the moment I'm mildly skeptical and uncertain of whether to twist or stick.

Very interesting that the LLM weights are co-evolved and reasoning skills improve!

What do you mean by this? I can't find anything there about modifying the used LLMs and the hosted ones wouldn't be possible to change. Do I misunderstand the convolved part you mentioned?

you are correct, on re-reading they only evolved the prompts ...

Some cool optimisations here: MAP elites, island models to prevent premature convergence & fast rejection of bad candidates.

What's particularly interesting is the meta level insight: The system discovered scipy.optimize.SLSQP for circle packing - a completely different algorithmic paradigm than it started with. It's genuinely discovering new approaches, not just parameter-tuning.

Sakana.ai improved on this by honing in on sample efficiency iirc with shinkaevolve (which is open source and not an ai slop project)

Yep, ShinkaEvolve described here: https://sakana.ai/shinka-evolve/ and available here: https://github.com/SakanaAI/ShinkaEvolve