Arbor introduces a multi-agent framework that enhances autonomous optimization in large action spaces. By utilizing a structured tree search as a cognition layer, it significantly improves inference throughput and stability compared to traditional methods. This advancement allows for more efficient coordination among agents, making it a game-changer for optimizing large language model performance. The implications could lead to substantial efficiency gains in AI-driven applications across various hardware platforms.