Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage that accumulates isolated facts for the purpose of condensing the lengthy inputs and generating new sub-queries through deduction. This static nature overlooks the crucial high-order correlations among primitive facts, the compositions of which can often provide stronger guidance for subsequent steps. Therefore, their representational strength and impact on multi-step reasoning and knowledge evolution are limited, resulting in fragmented reasoning and weak global sense-making capacity in extended contexts. We introduce HGMem, a hypergraph-based memory mechanism that extends the concept of memory beyond simple storage into a dynamic, expressive structure for complex reasoning and global understanding. In our approach, memory is represented as a hypergraph whose hyperedges correspond to distinct memory units, enabling the progressive formation of higher-order interactions within memory. This mechanism connects facts and thoughts around the focal problem, evolving into an integrated and situated knowledge structure that provides strong propositions for deeper reasoning in subsequent steps. We evaluate HGMem on several challenging datasets designed for global sense-making. Extensive experiments and in-depth analyses show that our method consistently improves multi-step RAG and substantially outperforms strong baseline systems across diverse tasks.

翻译：多步检索增强生成（RAG）已成为增强大语言模型在需要全局理解和深度推理任务上性能的广泛采用策略。许多RAG系统集成了工作记忆模块以整合检索到的信息。然而，现有记忆设计主要作为被动存储，其功能是通过演绎来累积孤立事实以压缩长输入并生成新的子查询。这种静态特性忽视了原始事实间关键的高阶关联，而这些关联的组合常能为后续步骤提供更强指导。因此，其表征能力以及对多步推理和知识演化的影响有限，导致在扩展上下文中出现碎片化推理和薄弱的全局意义构建能力。我们提出了HGMem，一种基于超图的记忆机制，将记忆概念从简单存储扩展为用于复杂推理和全局理解的动态、富有表达力的结构。在我们的方法中，记忆被表示为一个超图，其超边对应不同的记忆单元，从而能够在记忆内部逐步形成高阶交互。该机制围绕核心问题连接事实与思考，演化为一个集成且情境化的知识结构，为后续步骤的深度推理提供有力命题。我们在多个专为全局意义构建设计的挑战性数据集上评估了HGMem。大量实验和深入分析表明，我们的方法持续改进了多步RAG，并在多样化任务中显著超越了强基线系统。