GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
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. 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...
| Authors | Maureese Williams, Dymitr Nowicki |
|---|---|
| Primary category | cs.AI |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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