Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Quick Answer
Arbor introduces a multi-agent framework utilizing structured tree search for optimizing LLM inference, achieving up to 193% throughput-latency improvement compared to vendor-optimized systems.
Quick Take
It employs an Orchestrator and Critic agent for stability and coordination, demonstrating hardware-agnostic performance with minimal variance.
Key Points
- Arbor maintains a shared search tree of scored hypotheses for autonomous agents.
- Achieves 193% improvement in inference throughput-latency over traditional methods.
- Utilizes an Orchestrator and Critic agent for checks-and-balances architecture.
- Generalizes across multiple hardware generations with reproducible results.
- Single agent without Arbor shows only 33% throughput improvement before failure.
Paper Resources
Source Excerpt
Arbor is a framework that introduces structured tree search as a cognition layer for autonomous agents operating in large, stateful action spaces. Prior autonomous optimization systems operate on isolated targets with stateless evaluation. Arbor instead maintains an explicit search tree of scored hypotheses that serves as the shared working memory across agents, evolving with every measurement, treating failures as diagnostic signal that reshapes subsequent exploration, and expanding
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