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GATS introduces a Graph-Augmented Tree Search with a layered world model (symbolic, learned, generative) to eliminate LLM calls during planning, achieving 100% success on synthetic tasks and stress tests, outperforming LATS and ReAct.
This paper proposes a novel structured neuron pruning framework for deep neural networks using multi-armed bandit algorithms, demonstrating effectiveness on various tasks.