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Arbor: Tree Search as a Cognition Layer for Autonomous Agents

Neha Prakriya, Chaojun Hou, Zheng Gong, Huasha Zhao 2026-06-14
Search Cognition Autonomy × Agents

Arbor addresses the problem of autonomous optimization in large, stateful action spaces by introducing a multi-agent framework with structured tree search as a shared cognition layer. The method pairs an Orchestrator agent with a Critic agent in a checks-and-balances architecture, using an explicit search tree of scored hypotheses as working memory. Experimental evidence shows Arbor achieves up to 193% inference throughput-latency Pareto improvement over vendor-optimized baselines, while a single agent without the harness plateaus at +33% and crashes within hours. This matters because it enables fully autonomous, hardware-agnostic, and reproducible multi-day optimization campaigns across the full LLM inference stack.

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An LLM System for Autonomous Variational Quantum Circuit Design

Kenya Sakka, Wataru Mizukami, Kosuke Mitarai 2026-06-14
Language Model LLM Machine Learning Hardware Quant Ph

The problem is that designing high-performing quantum circuits remains heavily reliant on human expertise. The method introduces an autonomous agentic framework using LLMs with seven integrated components for iterative circuit design under explicit constraints. Experimental evidence shows the framework outperforms representative quantum feature maps on image classification and achieves competitive accuracy for molecular ground state estimation across seven molecules. This matters because it establishes LLM-driven agentic systems as a viable paradigm for automated quantum circuit design and demonstrates AI's role in iterative scientific optimization.

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