Retrieval-augmented generation (RAG) equips large language models (LLMs) with reliable knowledge memory. To strengthen cross-text associations, recent research integrates graphs and hypergraphs into RAG to capture pairwise and multi-entity relations as structured links. However, their misaligned memory organization necessitates costly, disjointed retrieval. To address these limitations, we propose IGMiRAG, a framework inspired by human intuition-guided reasoning. It constructs a hierarchical heterogeneous hypergraph to align multi-granular knowledge, incorporating deductive pathways to simulate realistic memory structures. During querying, IGMiRAG distills intuitive strategies via a question parser to control mining depth and memory window, and activates instantaneous memories as anchors using dual-focus retrieval. Mirroring human intuition, the framework guides retrieval resource allocation dynamically. Furthermore, we design a bidirectional diffusion algorithm that navigates deductive paths to mine in-depth memories, emulating human reasoning processes. Extensive evaluations indicate IGMiRAG outperforms the state-of-the-art baseline by 4.8% EM and 5.0% F1 overall, with token costs adapting to task complexity (average 6.3k+, minimum 3.0k+). This work presents a cost-effective RAG paradigm that improves both efficiency and effectiveness.

翻译：检索增强生成（RAG）为大型语言模型（LLM）提供了可靠的知识记忆。为加强跨文本关联，近期研究将图与超图融入RAG，以捕获成对及多实体关系作为结构化链接。然而，其未对齐的记忆组织方式导致检索过程成本高昂且相互割裂。为应对这些局限，我们提出IGMiRAG框架，其灵感来源于人类直觉引导的推理过程。该框架构建分层异质超图以对齐多粒度知识，并融入演绎路径以模拟真实的记忆结构。在查询过程中，IGMiRAG通过问题解析器提炼直觉策略，以控制挖掘深度与记忆窗口，并利用双焦点检索激活瞬时记忆作为锚点。该框架通过动态引导检索资源分配，模拟了人类的直觉机制。此外，我们设计了一种双向扩散算法，该算法沿演绎路径导航以挖掘深度记忆，从而模拟人类的推理过程。大量实验评估表明，IGMiRAG在整体上以4.8%的EM分数和5.0%的F1分数超越当前最优基线模型，且其令牌消耗能自适应任务复杂度（平均6.3k+，最低3.0k+）。本工作提出了一种兼顾效率与效果的轻量化RAG范式。