PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents

ArXi:2605.07039v1 Announce Type: new Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, where evaluations are expensive, and progress depends on learning task-specific search dynamics. We