Recent advances in Large Language Models (LLMs) have significantly improved complex reasoning capabilities. Retrieval-Augmented Generation (RAG) has further extended these capabilities by grounding generation in dynamically retrieved evidence, enabling access to information beyond the model's training parameters. However, while RAG addresses knowledge availability, standard pipelines treat retrieved documents as independent, unstructured text chunks, forcing models to implicitly connect information across fragmented context. This limitation becomes critical for multi-hop queries, where answering correctly requires synthesizing information scattered across different documents. We present Structure-Augmented Reasoning Generation (SARG), a post-retrieval framework that addresses this gap by materializing explicit reasoning structures from retrieved context. SARG operates in three stages: extracting relational triples from retrieved documents via few-shot prompting, organizing these triples into a domain-adaptive knowledge graph, and performing multi-hop traversal to identify relevant reasoning chains. These chains, along with their associated text chunks, are then integrated into the generation prompt to explicitly guide the model's reasoning process. Importantly, SARG doesn't require custom retrievers or domain-specific fine-tuning. Instead, it functions as a modular layer compatible with all existing RAG pipelines. Extensive experiments on open-domain QA benchmarks and specialized reasoning datasets in finance and medicine demonstrate that SARG significantly outperforms state-of-the-art flat-context RAG baselines in both factual accuracy and reasoning coherence. Furthermore, by surfacing the exact traversal paths used during generation, SARG provides fully traceable and interpretable inference.

翻译：近年来，大型语言模型（LLMs）在复杂推理能力方面取得了显著进展。检索增强生成（RAG）通过将生成过程建立在动态检索的证据之上，进一步扩展了这些能力，使得模型能够获取超出其训练参数范围的信息。然而，尽管RAG解决了知识可用性问题，标准流程通常将检索到的文档视为独立、非结构化的文本块，迫使模型在碎片化的上下文中隐式地连接信息。这一限制对于多跳查询尤为关键，因为正确回答此类问题需要综合散布在不同文档中的信息。本文提出结构增强推理生成（SARG），一种后检索框架，通过从检索到的上下文中构建显式的推理结构来弥补这一不足。SARG分三个阶段操作：通过少量示例提示从检索文档中提取关系三元组，将这些三元组组织成一个领域自适应的知识图谱，并执行多跳遍历以识别相关的推理链。随后，这些推理链及其关联的文本块被整合到生成提示中，以显式地指导模型的推理过程。重要的是，SARG不需要定制化的检索器或领域特定的微调。相反，它作为一个模块化层，与所有现有的RAG流程兼容。在开放域问答基准以及金融和医学领域的专业推理数据集上进行的大量实验表明，SARG在事实准确性和推理连贯性方面均显著优于最先进的扁平上下文RAG基线。此外，通过揭示生成过程中使用的确切遍历路径，SARG提供了完全可追溯且可解释的推理过程。