IGMiRAG: 基于直觉引导的检索增强生成与深度记忆适应性挖掘
IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory
AI总结：
IGMiRAG通过直觉引导的检索增强生成框架，结合超图和演绎路径，提升多粒度知识对齐和深度记忆挖掘，实现更高效有效的生成性能。
摘要：
检索增强生成（RAG）使大语言模型（LLMs）具备可靠的知识记忆。为加强跨文本关联，近期研究将图和超图整合到RAG中，以捕捉成对和多实体关系作为结构链接。然而，其记忆组织不一致需要昂贵且不连贯的检索。为解决这些限制，我们提出IGMiRAG，受人类直觉引导推理启发。它构建了分层异构超图以对齐多粒度知识，结合演绎路径以模拟真实记忆结构。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和记忆窗口，并通过双焦点检索激活即时记忆作为锚点。模仿人类直觉，该框架动态引导检索资源分配。此外，我们设计了一种双向扩散算法，通过导航演绎路径挖掘深度记忆，模拟人类推理过程。大量评估表明，IGMiRAG在总体上比最先进的基线高出4.8%的EM和5.0%的F1，token成本适应任务复杂性（平均6.3k+，最小3.0k+）。这项工作提出了一种成本有效的RAG范式，提高了效率和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
