Arbor: Multi-Agent Framework Uses Tree Search to Optimize LLM Inference Performance
Researchers have introduced Arbor, a multi-agent AI framework that uses structured tree search as a 'cognition layer' to autonomously optimize large language model inference performance over extended campaigns. Unlike prior systems that evaluate isolated targets without memory, Arbor maintains a shared search tree of scored hypotheses that evolves with each measurement, treating failures as diagnostic signals. The system achieved up to 193% improvement in inference throughput-latency over vendor-optimized baselines, far outpacing a single agent without the framework, which plateaued at 33% and crashed within hours.
Arbor is a multi-agent framework presented in a preprint on arXiv that introduces tree search as a structured cognition layer for autonomous agents working in large, stateful action spaces. The system is validated on full-stack LLM inference optimization — a domain historically requiring coordinated effort from engineering teams spanning application, framework, compiler, kernel, and hardware layers. Arbor's architecture pairs an Orchestrator agent, which delegates tasks to Domain Specialists across the inference stack, with a Critic agent responsible for root-cause analysis, introspection, and measurement validation, creating a checks-and-balances structure where neither agent can unilaterally drive the system. Agent capabilities are divided into 'hard skills' (domain expertise) and 'soft skills' (coordination protocols governing how contributions compose), enabling fully autonomous multi-day optimization runs. The framework achieved up to 193% improvement on inference throughput-latency Pareto metrics over vendor-optimized baselines, compared to just 33% for a single agent operating without the harness, which also crashed irrecoverably within hours. Arbor demonstrated generalization across multiple hardware generations with run-to-run variance within 2 percentage points, suggesting hardware-agnostic reproducibility.
What's missing
The paper is a preprint and has not yet undergone peer review. Key open questions include whether the 193% throughput-latency improvement holds across diverse LLM architectures and workloads beyond those tested, what the computational cost of running Arbor's multi-agent campaigns is, and how the framework performs in domains outside LLM inference optimization.
What different sources said
- arXiv cs.AICenter
Arbor: Tree Search as a Cognition Layer for Autonomous Agents
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