Arbor: Tree Search as a Cognition Layer for Autonomous Agents

2026-04-26 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Arbor is a multi-agent framework that integrates structured tree search as a cognition layer for autonomous agents optimizing performance in large, stateful action spaces. Unlike prior systems, Arbor maintains an explicit search tree of scored hypotheses as shared memory, treating failures as diagnostic signals and expanding exploration as bottlenecks shift. Validated on full-stack LLM inference optimization, Arbor features an Orchestrator agent driving optimization and a Critic agent safeguarding stability through root-cause analysis and measurement validation, forming a checks-and-balances architecture. This framework achieved up to 193% inference throughput-latency Pareto improvement over vendor-optimized baselines on AMD Instinct MI355X GPUs, with single agents failing or crashing. It generalizes across hardware generations like MI300X and demonstrates reproducibility within 2 percentage points.

Key takeaway

For AI Architects designing autonomous optimization systems, Arbor demonstrates that integrating a stateful tree search with a checks-and-balances multi-agent architecture is crucial for sustained, cross-layer performance gains. You should prioritize explicit tension between aggressive optimization and stability, ensuring diagnostic feedback from failures reshapes future exploration. This approach enables robust, multi-day campaigns, significantly outperforming single-agent or unstructured methods.

Key insights

Arbor uses multi-agent tree search with checks and balances to autonomously optimize complex, multi-layer systems.

Principles

• Maintain an explicit search tree as shared working memory.
• Treat failures as diagnostic signals to reshape exploration.
• Decompose agent capabilities into hard and soft skills.

Method

The Orchestrator executes a depth-first search loop, profiling bottlenecks, scoring candidate actions, dispatching to dynamic Domain Specialists, and updating the tree based on outcomes, with the Critic providing stability and diagnosis.

In practice

• Apply to full-stack LLM inference optimization.
• Use dynamic agent construction for specialized tasks.
• Integrate a persistent knowledge base for warm-start transfer.

Topics

• Autonomous Agents
• Tree Search
• LLM Inference Optimization
• Multi-Agent Systems
• Performance Optimization
• AMD Instinct GPUs

Best for: MLOps Engineer, AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.