GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance. Our three-layer world model integrates: (L1) exact symbolic action matching, (L2) statistics learned from execution logs, and (L3) LLM-based prediction for unknown actions. On synthetic planning tasks with branching paths and dead-ends, GATS achieves \textbf{100\% success rate} compared to 92 % for LATS and 64\% for ReAct.
- ▪We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance.
- ▪Our three-layer world model integrates: (L1) exact symbolic action matching, (L2) statistics learned from execution logs, and (L3) LLM-based prediction for unknown actions.
- ▪On synthetic planning tasks with branching paths and dead-ends, GATS achieves \textbf{100\% success rate} compared to 92 % for LATS and 64\% for ReAct.
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Computer Science > Artificial Intelligence arXiv:2607.08894 (cs) [Submitted on 9 Jul 2026] Title:GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning Authors:Maureese Williams, Dymitr Nowicki View a PDF of the paper titled GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning, by Maureese Williams and Dymitr Nowicki View PDF HTML (experimental) Abstract:Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior.
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