193% inference throughput-latency Pareto improvement over vendor-optimized baselines — that's what Arbor delivers by introducing structured tree search as a cognition layer for autonomous agents. Prior systems work on isolated targets with stateless evaluation, hitting a wall fast. Arbor instead maintains an explicit search tree of scored hypotheses as shared working memory across agents, evolving with every measurement.
Why Stateless Optimization Fails
A single agent without Arbor's harness plateaus at just 33% throughput improvement and crashes irrecoverably within hours. The problem isn't the agent — it's the lack of structured memory and failure diagnosis. Arbor treats failures as diagnostic signal that reshapes subsequent exploration, and prior successes shift the bottleneck distribution, so the system keeps learning instead of spinning out.
Full-stack LLM inference optimization has historically required coordinated effort from engineering teams across the application, framework, compiler, kernel, and hardware stack. Arbor replaces that human coordination with a multi-agent architecture that decomposes capabilities into hard skills (domain expertise) and soft skills (coordination protocols that determine how contributions compose).
The Checks-and-Balances Architecture
Arbor pairs an Orchestrator agent that 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. Neither agent can unilaterally drive the system. This separation prevents the kind of runaway behavior that kills single agents inside hours.
I've seen enough naive agent systems spiral into infinite loops or catastrophic resource consumption. Arbor's explicit tree search acts as a forcing function: every hypothesis gets scored, every failure gets logged and used, and the search tree itself becomes the shared working memory that keeps all agents aligned.
Hardware-Agnostic and Reproducible Results
Arbor generalizes to multiple generations of hardware platform. Run-to-run variance is within 2 percentage points — that's reproducibility you can bet an optimization pipeline on. The method is hardware-agnostic, meaning I don't need to rewire the framework every time a new GPU or accelerator ships.
The paper demonstrates this on LLM inference, but the architecture — tree search as a cognition layer with checks-and-balances — applies to any domain with large, stateful action spaces where autonomous exploration is currently done blind. Arbor's next move is to shift the bottleneck from manual tuning to structured, reproducible automation.
Source: Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Domain: arxiv.org
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