用目标导向图提升大模型在Minecraft中的多步推理能力
From Entity-Centric to Goal-Oriented Graphs: Enhancing LLM Knowledge Retrieval in Minecraft
- 用目标节点和逻辑依赖边构建目标导向图
- 在Minecraft测试中显著优于GraphRAG等基线方法
- 适合需要复杂规划的智能体与交互式系统研究者
大语言模型虽具备强大通用能力，但在复杂交互环境中常难以进行多步程序化推理。现有基于图的检索增强方法（如GraphRAG）因实体-关系图碎片化，难以构建连贯的多步计划。本文提出目标导向图（GoG）框架，每个节点代表一个目标，边表示目标间的逻辑依赖。该结构支持通过识别高层目标并递归检索其前置条件，显式获取因果推理路径，从而引导大模型完成任务。在要求强多步规划能力且蕴含丰富程序知识的Minecraft测试平台上，实验表明GoG显著提升程序化推理性能，大幅超越GraphRAG及其他先进基线方法。
原文摘要 · Abstract (English)
Large Language Models (LLMs) demonstrate impressive general capabilities but often struggle with step-by-step procedural reasoning, a critical challenge in complex interactive environments. While retrieval-augmented methods like GraphRAG attempt to bridge this gap, their fragmented entity-relation graphs hinder the construction of coherent, multi-step plans. In this paper, we propose a novel framework based on Goal-Oriented Graphs (GoGs), where each node represents a goal and edges encode logical dependencies between them. This structure enables the explicit retrieval of causal reasoning paths by identifying a high-level goal and recursively retrieving its prerequisites, forming a coherent chain to guide the LLM. Through extensive experiments on the Minecraft testbed, a domain that demands robust multi-step planning and provides rich procedural knowledge, we demonstrate that GoG substantially improves procedural reasoning and significantly outperforms GraphRAG and other state-of-the-art baselines.
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