A research paper proposes Learning-to-Optimize (L2O) as a missing architectural layer for AI-native networks, enabling systematic generation, transfer, and exploitation of optimization knowledge by AI models. The paper introduces a four-stage L2O workflow and demonstrates its application to a relay-selection problem in NR-V2X communication systems.