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.

翻译：检索增强生成（RAG）通过整合外部知识来增强大语言模型（LLMs）。近期，一些工作将迭代式知识积累过程引入RAG模型，以逐步积累和精炼与查询相关的知识，从而构建更全面的知识表示。然而，这些迭代过程通常缺乏连贯的组织结构，这限制了构建更全面、更具凝聚力的知识表示。为解决此问题，我们提出了PAGER，一个面向RAG的页面驱动的自主知识表示框架。PAGER首先提示一个LLM为给定问题构建一个结构化的认知大纲，该大纲由代表不同知识方面的多个槽位组成。然后，PAGER迭代地检索并精炼相关文档以填充每个槽位，最终构建一个连贯的页面，作为指导答案生成的上下文输入。在多个知识密集型基准测试和骨干模型上的实验表明，PAGER始终优于所有RAG基线方法。进一步的分析表明，PAGER构建了更高质量和信息密度更高的知识表示，更好地缓解了知识冲突，并使LLMs能够更有效地利用外部知识。所有代码可在 https://github.com/OpenBMB/PAGER 获取。