IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory

• URL: http://arxiv.org/abs/2602.07525v1
• Date: Sat, 07 Feb 2026 12:42:31 GMT
• Title: IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory
• Authors: Xingliang Hou, Yuyan Liu, Qi Sun, haoxiu wang, Hao Hu, Shaoyi Du, Zhiqiang Tian,
• Abstract summary: Retrieval-augmented generation (RAG) equips large language models with reliable knowledge memory.<n>Recent research integrates graphs and hypergraphs into RAG to capture pairwise and multi-entity relations as structured links.<n>We propose IGMiRAG, a framework inspired by human intuition-guided reasoning.
• Score: 33.00642870872058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG [53.30561659838455]
  Large Language Models (LLMs) often struggle with inherent knowledge boundaries and hallucinations.<n>Retrieval-Augmented Generation (RAG) frequently overlooks structural interdependencies essential for multi-hop reasoning.<n>Help achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$times$ speedup over leading Graph-based RAG baselines.
  arXiv  Detail & Related papers  (2026-02-24T14:05:29Z)
• VimRAG: Navigating Massive Visual Context in Retrieval-Augmented Generation via Multimodal Memory Graph [42.348770377488094]
  VimRAG is a framework tailored for multimodal Retrieval-augmented Reasoning across text, images, and videos.<n>We propose a Graph-Guided Policy Optimization strategy to disentangle step-wise validity from trajectory-level rewards.<n>Experiments demonstrate that VimRAG consistently achieves state-of-the-art performance on diverse multimodal RAG benchmarks.
  arXiv  Detail & Related papers  (2026-02-13T09:05:09Z)
• Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion [14.538534837583931]
  Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge.<n>We propose CogitoRAG, a RAG framework that simulates human cognitive memory processes.<n>We show that CogitoRAG significantly outperforms state-of-the-art RAG methods, showcasing superior capabilities in complex knowledge integration and reasoning.
  arXiv  Detail & Related papers  (2026-02-11T12:58:08Z)
• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling [83.29209853451697]
  Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs)<n>We introduce HGMem, a hypergraph-based memory mechanism that extends the concept of memory into a dynamic, expressive structure for complex reasoning and global understanding.<n>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.
  arXiv  Detail & Related papers  (2025-12-30T03:13:10Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• EcphoryRAG: Re-Imagining Knowledge-Graph RAG via Human Associative Memory [0.0]
  We introduce EcphoryRAG, an entity-centric knowledge graph RAG framework.<n>During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata.<n>For retrieval, the system first extracts cue entities from queries, then performs a scalable multi-hop associative search.
  arXiv  Detail & Related papers  (2025-10-10T03:07:27Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps [16.84310001807895]
  This paper introduces a model-agnostic approach that can be applied to A-RAG methods.<n>Specifically, we use cache access and parallel generation to speed up the prefilling and decoding stages respectively.
  arXiv  Detail & Related papers  (2025-05-19T05:39:38Z)
• From RAG to Memory: Non-Parametric Continual Learning for Large Language Models [6.380729797938521]
  retrieval-augmented generation (RAG) has become the dominant way to introduce new information.<n>Recent RAG approaches augment vector embeddings with various structures like knowledge graphs to address some gaps, namely sense-making and associativity.<n>We propose HippoRAG 2, a framework that outperforms standard RAG comprehensively on factual, sense-making, and associative memory tasks.
  arXiv  Detail & Related papers  (2025-02-20T18:26:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.