Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Arbor addresses the problem of autonomous optimization in large, stateful action spaces by introducing a multi-agent framework with structured tree search as a shared cognition layer. The method pairs an Orchestrator agent with a Critic agent in a checks-and-balances architecture, using an explicit search tree of scored hypotheses as working memory. Experimental evidence shows Arbor achieves up to 193% inference throughput-latency Pareto improvement over vendor-optimized baselines, while a single agent without the harness plateaus at +33% and crashes within hours. This matters because it enables fully autonomous, hardware-agnostic, and reproducible multi-day optimization campaigns across the full LLM inference stack.