Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Arbor is a multi-agent framework introducing structured tree search as a cognition layer for autonomous agents in large stateful action spaces. Validated on full-stack LLM inference optimization, it achieves up to 193% Pareto improvement in throughput-latency over vendor baselines, with a critic agent ensuring stability.
[2606.12563] Arbor: Tree Search as a Cognition Layer for Autonomous Agents
[Submitted on 10 Jun 2026]
Title:Arbor: Tree Search as a Cognition Layer for Autonomous Agents
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Abstract:Arbor is a multi-agent 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 as prior successes shift the bottleneck distribution.
We validate Arbor on full-stack LLM inference optimization, a domain where achieving peak performance has historically required coordinated effort from engineering teams across the application, framework, compiler, kernel, and hardware stack. Arbor pairs an Orchestrator agent, which drives optimization by delegating to Domain Specialists across the inference stack, with a Critic agent that safeguards stability through root-cause analysis, introspection, and measurement validation -- a checks-and-balances architecture where neither agent can unilaterally drive the system. Agent capabilities are decomposed into hard skills (domain expertise) and soft skills (coordination protocols that determine how contributions compose), enabling fully autonomous multi-day campaigns. Arbor achieves up to 193% inference throughput-latency Pareto improvement over vendor-optimized baselines, while a single agent without the harness plateaus at +33% throughput improvement and crashes irrecoverably within hours. Arbor generalizes to multiple generations of hardware platform, and run-to-run variance is within 2 percentage points demonstrating that the method is hardware-agnostic and reproducible.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12563 [cs.AI]
(or arXiv:2606.12563v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12563
arXiv-issued DOI via DataCite (pending registration)
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From: Neha Prakriya [view email] [v1] Wed, 10 Jun 2026 18:14:56 UTC (300 KB)
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