LinkEvo: Learning When to Recombine Modular Prompts

Genetic prompt optimization improves modular language-model systems by evolving instruction bodies while preserving their interfaces, tools, and control flow. However, recombination can disrupt dependencies between modules, and the value of inheriting an instruction block changes with its recipient context and evaluation cost. We introduce LinkEvo, a bounded-population genetic optimizer that measures interactions through controlled block substitutions, builds candidate groups from these measurements, and allocates mutation and crossover effort using context-weighted signed gate gains under an explicit adaptation budget. Across six benchmarks and six executor families, LinkEvo achieved positive observed differences in 34 of 36 settings against uniform genetic-action allocation and 33 against GEPA with merging, with median gains of 1.59 and 0.82 percentage points, respectively, and Qwen3.6 retrieval gains over GEPA with merging of 1.20 points on HotpotQA and 1.33 on HoVer. These results identify a practical role for optimization history in learning which instruction combinations to inherit and when their predicted utility warrants further evaluation, supporting gains across several model families while exposing small mixed differences on reasoning tasks.