Knowledge Retrieval in LLM Gaming: A Shift from Entity-Centric to Goal-Oriented Graphs

Large Language Models (LLMs) demonstrate impressive general capabilities but often struggle with step-by-step reasoning, especially in complex applications such as games. While retrieval-augmented methods like GraphRAG attempt to bridge this gap through cross-document extraction and indexing, their fragmented entity-relation graphs and overly dense local connectivity hinder the construction of coherent reasoning. In this paper, we propose a novel framework based on Goal-Oriented Graphs (GoGs), where each node represents a goal and its associated attributes, and edges encode logical dependencies between goals. This structure enables explicit retrieval of reasoning paths by first identifying high-level goals and recursively retrieving their subgoals, forming coherent reasoning chains to guide LLM prompting. Our method significantly enhances the reasoning ability of LLMs in game-playing tasks, as demonstrated by extensive experiments on the Minecraft testbed, outperforming GraphRAG and other baselines.

@article{leung2025_2505.18607,
  title={ Knowledge Retrieval in LLM Gaming: A Shift from Entity-Centric to Goal-Oriented Graphs },
  author={ Jonathan Leung and Yongjie Wang and Zhiqi Shen },
  journal={arXiv preprint arXiv:2505.18607},
  year={ 2025 }
}