Evo2Team: When Do Evolved Skills Transfer? From Selection to Deployment

A skill bank that helps one multi-agent system may leave another's behavior unchanged. We study routing and communication skill transfer across Count-Frequency and AgentsNet teams of 4–32 agents using GPT and Qwen model ladders. Source evolution meets a joint quality, cost, and confirmation goal in 14 of 16 settings. We then evaluate Evo2Team, which selects, adapts, and confirms evolved skills on the target, alongside six frozen selectors in 28 directions. Evo2Team's exploration costs less than new target evolution in every direction, even when reused reference runs are charged. Yet different selected banks produce identical AgentsNet execution in two directions. When Evo2Team changes execution, its gains can be broad: one Count-Frequency direction improves 28 of 32 tasks over KNN. Seven positive AgentsNet held-out outcomes save 6.1–14.6% in deployment cost while using transferred skills on only three to six of fifteen tasks. In five earlier accepted directions, 22 transferred task records pass three fixed-graph confirmations, but four fail in recorded new-graph executions, where graphs and model responses both change. These results locate transfer benefit in executed decisions, task coverage, and final quality and cost.