Structured Knowledge Representation through Contextual Pages for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2601.09402v1
• Date: Wed, 14 Jan 2026 11:44:31 GMT
• Title: Structured Knowledge Representation through Contextual Pages for Retrieval-Augmented Generation
• Authors: Xinze Li, Zhenghao Liu, Haidong Xin, Yukun Yan, Shuo Wang, Zheni Zeng, Sen Mei, Ge Yu, Maosong Sun,
• Abstract summary: PAGER is a page-driven autonomous knowledge representation framework for RAG.<n>It iteratively retrieves and refines relevant documents to populate each slot, ultimately constructing a coherent page.<n>Experiments show that PAGER consistently outperforms all RAG baselines.
• Score: 53.768256130061765
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge. Recently, some works have incorporated iterative knowledge accumulation processes into RAG models to progressively accumulate and refine query-related knowledge, thereby constructing more comprehensive knowledge representations. However, these iterative processes often lack a coherent organizational structure, which limits the construction of more comprehensive and cohesive knowledge representations. To address this, we propose PAGER, a page-driven autonomous knowledge representation framework for RAG. PAGER first prompts an LLM to construct a structured cognitive outline for a given question, which consists of multiple slots representing a distinct knowledge aspect. Then, PAGER iteratively retrieves and refines relevant documents to populate each slot, ultimately constructing a coherent page that serves as contextual input for guiding answer generation. Experiments on multiple knowledge-intensive benchmarks and backbone models show that PAGER consistently outperforms all RAG baselines. Further analyses demonstrate that PAGER constructs higher-quality and information-dense knowledge representations, better mitigates knowledge conflicts, and enables LLMs to leverage external knowledge more effectively. All code is available at https://github.com/OpenBMB/PAGER.

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