Pairs language-model variation with evaluators to evolve interpretable programs.
A language model proposes a program. An automatic evaluator scores it. The best programs go back into the prompt and the loop runs again. The model supplies variation and the evaluator supplies selection.
It produced a new largest known construction for the cap set problem, an open question in extremal combinatorics, and bin-packing heuristics that beat the standard ones.
The output is a readable program rather than a weight update, so a mathematician can look at the discovery and check why it works. That is the part worth copying.