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!
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)
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!
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)