Large language models (LLMs) have achieved impressive performance on knowledge-intensive tasks, yet they often struggle with multi-step reasoning due to the unstructured nature of retrieved context. While retrieval-augmented generation (RAG) methods provide external information, the lack of explicit organization among retrieved passages limits their effectiveness, leading to brittle reasoning pathways. Recent interpretability studies highlighting the importance of structured intermediate reasoning further align with this perspective. We propose Retrieval-And-Structuring (RAS), a framework that dynamically constructs question-specific knowledge graphs through iterative retrieval and structured knowledge building. RAS interleaves targeted retrieval planning with incremental graph construction, enabling models to assemble and reason over evolving knowledge structures tailored to each query. On seven knowledge-intensive benchmarks, RAS consistently outperforms strong baselines, achieving up to 8.7\% and 7.0\% gains with proprietary and open-source LLMs, respectively. Our results demonstrate that dynamic, question-specific knowledge structuring offers a robust path to improving reasoning accuracy and robustness in language model generation.

翻译：大语言模型在知识密集型任务上已展现出卓越性能，但由于检索上下文缺乏结构化，其在多步推理任务中仍面临挑战。虽然检索增强生成方法能够提供外部信息，但检索段落间缺乏显式组织限制了其有效性，导致推理路径脆弱易断。近期可解释性研究强调了结构化中间推理的重要性，进一步印证了这一观点。我们提出检索与结构化框架，该框架通过迭代检索与结构化知识构建，动态构建面向特定问题的知识图谱。RAS将定向检索规划与增量图谱构建交织进行，使模型能够针对每个查询组装并推理不断演化的知识结构。在七个知识密集型基准测试中，RAS始终优于现有强基线方法，在使用专有和开源大语言模型时分别实现了最高8.7%和7.0%的性能提升。我们的研究结果表明，动态的、面向特定问题的知识结构化方法为提高语言模型生成的推理准确性与鲁棒性提供了有效途径。