Cross-Model Reproductive Fitness: Evolving Transferable Skills across Model Hosts

Large language model agents increasingly use reusable skills to perform long-horizon tasks involving planning, tool use, and artifact manipulation. Existing evolution protocols typically optimize skills on a primary host and evaluate transfer after optimization, even though skills can transfer across execution models and agent harnesses. This selection procedure can favor host-specific instructions and invocation patterns, and pooled performance can conceal degradation on weaker or incompatible hosts. We introduce Cross-Model Reproductive Fitness (CMRF), a transfer-aware evolutionary framework that models model-harness configurations as heterogeneous host environments and selects skills directly for cross-host utility. CMRF organizes hosts into evolution islands and evaluates candidate skills based on aggregate task gain, cross-host variability, weak-model gain, and worst-host performance. Skills migrate between islands and receive reproductive credit when they provide measurable utility in the destination host, aligning evolutionary survival with transferability. CMRF decomposes each skill into a platform-agnostic core, a model adapter, and a harness invocation layer, preserving shared procedures while supporting host-specific adaptation. Under matched execution budgets, we evaluate CMRF on seen and held-out host configurations against single-host evolution, pooled-average selection, worst-host-aware selection, and island migration. Ablation studies and structural analyses isolate the contributions of migration, transfer-aware fitness, and layered skill design to cross-host robustness.