R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop
This paper proposes a new method for agent orchestration that improves performance by allowing agents to learn from self-play and skill withdrawal, and shows it outperforms existing methods on various tasks.