LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora

• URL: http://arxiv.org/abs/2510.10114v4
• Date: Sun, 09 Nov 2025 11:23:09 GMT
• Title: LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
• Authors: Luyao Zhuang, Shengyuan Chen, Yilin Xiao, Huachi Zhou, Yujing Zhang, Hao Chen, Qinggang Zhang, Xiao Huang,
• Abstract summary: Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge.<n>Existing graph-based RAG methods rely on unstable and costly relation extraction for graph construction.<n>We propose LinearRAG, an efficient framework that enables reliable graph construction and precise passage retrieval.
• Score: 17.929144506419064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with large-scale, unstructured corpora where information is fragmented. Recent advances incorporate knowledge graphs to capture relational structures, enabling more comprehensive retrieval for complex, multi-hop reasoning tasks. However, existing graph-based RAG (GraphRAG) methods rely on unstable and costly relation extraction for graph construction, often producing noisy graphs with incorrect or inconsistent relations that degrade retrieval quality. In this paper, we revisit the pipeline of existing GraphRAG systems and propose LinearRAG (Linear Graph-based Retrieval-Augmented Generation), an efficient framework that enables reliable graph construction and precise passage retrieval. Specifically, LinearRAG constructs a relation-free hierarchical graph, termed Tri-Graph, using only lightweight entity extraction and semantic linking, avoiding unstable relation modeling. This new paradigm of graph construction scales linearly with corpus size and incurs no extra token consumption, providing an economical and reliable indexing of the original passages. For retrieval, LinearRAG adopts a two-stage strategy: (i) relevant entity activation via local semantic bridging, followed by (ii) passage retrieval through global importance aggregation. Extensive experiments on four datasets demonstrate that LinearRAG significantly outperforms baseline models. Our code and datasets are available at https://github.com/DEEP-PolyU/LinearRAG.

Related papers

• AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction [60.51319139563509]
  We introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL)<n>We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices.<n>Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically good'' graphs to building demonstrably useful'' ones.
  arXiv  Detail & Related papers  (2025-10-17T06:03:36Z)
• GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning [50.40400074353263]
  Graph Neural Networks (GNNs) are powerful tools for precessing relational data but often struggle to generalize to unseen graphs.<n>We introduce textbfGraph textbfIn-context textbfL textbfTransformer (GILT), a framework built on an LLM-free and tuning-free architecture.
  arXiv  Detail & Related papers  (2025-10-06T08:09:15Z)
• G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge [88.82814893945077]
  Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge.<n>Recent graph-enhanced RAG (GraphRAG) attempts to bridge this gap by constructing tailored graphs and enabling LLMs to reason on them.<n>G-reasoner is a unified framework that integrates graph and language foundation models for reasoning over diverse graph-structured knowledge.
  arXiv  Detail & Related papers  (2025-09-29T04:38:12Z)
• Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning [32.78218766121055]
  Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning.<n>We propose a vertically unified agentic paradigm, Youtu-GraphRAG, to jointly connect the entire framework as an intricate integration.
  arXiv  Detail & Related papers  (2025-08-27T13:13:20Z)
• You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures [16.867592142212203]
  Large language models (LLMs) often suffer from hallucination, generating factually incorrect statements when handling questions beyond their knowledge.<n>Retrieval-augmented generation (RAG) addresses this by retrieving query-relevant contexts from knowledge bases to support LLM reasoning.<n>Existing Graph-based RAG methods rely on a costly process to transform the corpus into a graph, introducing overwhelming token cost and update latency.<n>We propose LogicRAG that dynamically extracts reasoning structures at inference time to guide adaptive retrieval without any pre-built graph.
  arXiv  Detail & Related papers  (2025-08-08T08:07:40Z)
• Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning [20.05893083101089]
  Graph-R1 is an agentic GraphRAG framework via end-to-end reinforcement learning (RL)<n>It introduces lightweight knowledge hypergraph construction, models retrieval as a multi-turn agent-environment interaction.<n>Experiments on standard RAG datasets show that Graph-R1 outperforms traditional GraphRAG and RL-enhanced RAG methods in reasoning accuracy, retrieval efficiency, and generation quality.
  arXiv  Detail & Related papers  (2025-07-29T15:01:26Z)
• 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)
• When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation [31.930889441883732]
  Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge.<n>Recent studies report that GraphRAG frequently underperforms vanilla RAG on many real-world tasks.<n>This raises a critical question: Is GraphRAG really effective, and in which scenarios do graph structures provide measurable benefits for RAG systems?
  arXiv  Detail & Related papers  (2025-06-06T02:37:47Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [79.75818239774952]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes [25.173078967881803]
  Retrieval-augmented generation (RAG) empowers large language models to access external and private corpus.<n>Current graph-based RAG approaches seldom prioritize the design of graph structures.<n>Inadequately designed graph not only impede the seamless integration of diverse graph algorithms but also result in workflow inconsistencies.<n>We propose NodeRAG, a graph-centric framework introducing heterogeneous graph structures.
  arXiv  Detail & Related papers  (2025-04-15T18:24:00Z)
• RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
  We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline.<n>RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components.<n>Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
  arXiv  Detail & Related papers  (2025-03-25T03:21:48Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)

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.