PAGE-RAG: Evidence-Grounded Adaptive Graph Retrieval for Long-Document Question Answering

• URL: http://arxiv.org/abs/2607.19301v1
• Date: Tue, 21 Jul 2026 17:13:22 GMT
• Title: PAGE-RAG: Evidence-Grounded Adaptive Graph Retrieval for Long-Document Question Answering
• Abstract summary: GraphRAG improves long-document question answering by introducing structured representations beyond conventional retrieval.<n>We propose PAGE-RAG, a projection-aware adaptive graph retrieval framework.<n>Experiments demonstrate that PAGE-RAG achieves accessible competitive answer quality while improving knowledge reliability.
• Score: 33.353114889949545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GraphRAG improves long-document question answering by introducing structured representations beyond conventional retrieval. However, automatically constructed graphs are inherently incomplete projections of source documents, and treating them as independent knowledge sources may lead to unreliable retrieval and generation. We propose PAGE-RAG, a projection-aware adaptive graph retrieval framework for reliable long-document question answering. PAGE-RAG views graph structures as semantic skeletons that organize and navigate document knowledge, rather than replacing the original knowledge source. Based on this perspective, PAGE-RAG introduces a task-adaptive retrieval routing strategy that dynamically selects appropriate retrieval behaviors according to query requirements. Furthermore, PAGE-RAG incorporates strict knowledge boundary control, ensuring that generated responses remain grounded within available evidence and abstaining from unsupported information beyond the accessible knowledge scope. Experiments demonstrate that PAGE-RAG achieves competitive answer quality while improving retrieval efficiency and knowledge reliability, highlighting the importance of projection-aware graph modeling, adaptive retrieval, and explicit knowledge boundary control for trustworthy GraphRAG systems. The source code is publicly available at https://github.com/CXY0112/PAGE-RAG.

Related papers

• SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation [10.08021023788401]
  SelfGraphRAG is a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever.<n>We show that SelfGraphRAG improves retrieval precision and downstream reasoning performance over embedding-based baselines.
  arXiv  Detail & Related papers  (2026-08-25T20:18:05Z)
• NaviRAG: Towards Active Knowledge Navigation for Retrieval-Augmented Generation [50.16741209529908]
  NaviRAG is a novel framework that shifts from passive segment retrieval to active knowledge navigation.<n>We show that NaviRAG consistently improves both retrieval recall and end-to-end answer performance over conventional RAG baselines.
  arXiv  Detail & Related papers  (2026-04-14T14:07:01Z)
• Graph-Anchored Knowledge Indexing for Retrieval-Augmented Generation [53.42323544075114]
  We propose GraphAnchor, a novel Graph-Anchored Knowledge Indexing approach.<n> Experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of GraphAnchor.
  arXiv  Detail & Related papers  (2026-01-23T05:41:05Z)
• LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding [37.12229829548839]
  We propose LAD-RAG, a novel layout-Aware Dynamic RAG framework.<n>LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies.<n>Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning.
  arXiv  Detail & Related papers  (2025-10-08T17:02:04Z)
• Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on Math Textbook [6.356031718676495]
  Large language models (LLMs) have emerged as novel aids for information retrieval during learning.<n>We investigate Retrieval-Augmented Generation (RAG) and GraphRAG, a knowledge graph-enhanced RAG approach, for page-level question answering.
  arXiv  Detail & Related papers  (2025-09-20T19:06:49Z)
• 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)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• RAG vs. GraphRAG: A Systematic Evaluation and Key Insights [53.83444096699458]
  We systematically evaluate Retrieval-Augmented Generation (RAG) and GraphRAG on text-based benchmarks.<n>Our results highlight the distinct strengths of RAG and GraphRAG across different tasks and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-02-17T02:36:30Z)
• CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs [9.718354494802002]
  Contextualized Graph Retrieval-Augmented Generation (CG-RAG) is a novel framework that integrates sparse and dense retrieval signals within graph structures.<n>First, we propose a contextual graph representation for citation graphs, effectively capturing both explicit and implicit connections within and across documents.<n>Second, we introduce Lexical-Semantic Graph Retrieval (LeSeGR), which seamlessly integrates sparse and dense retrieval signals with graph encoding.<n>Third, we present a context-aware generation strategy that utilizes the retrieved graph-structured information to generate precise and contextually enriched responses.
  arXiv  Detail & Related papers  (2025-01-25T04:18:08Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information.<n>Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information.<n>Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• Graph Retrieval-Augmented Generation: A Survey [28.979898837538958]
  Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.
  arXiv  Detail & Related papers  (2024-08-15T12:20:24Z)

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.