5G technology enables mobile Internet access for billions of users. Answering expert-level questions about 5G specifications requires navigating thousands of pages of cross-referenced standards that evolve across releases. Existing retrieval-augmented generation (RAG) frameworks, including telecom-specific approaches, rely on semantic similarity and cannot reliably resolve cross-references or reason about specification evolution. We present DeepSpecs, a RAG system enhanced by structural and temporal reasoning via three metadata-rich databases: SpecDB (clause-aligned specification text), ChangeDB (line-level version diffs), and TDocDB (standardization meeting documents). DeepSpecs explicitly resolves cross-references by recursively retrieving referenced clauses through metadata lookup, and traces specification evolution by mining changes and linking them to Change Requests that document design rationale. We curate two 5G QA datasets: 573 expert-annotated real-world questions from practitioner forums and educational resources, and 350 evolution-focused questions derived from approved Change Requests. Across multiple LLM backends, DeepSpecs outperforms base models and state-of-the-art telecom RAG systems; ablations confirm that explicit cross-reference resolution and evolution-aware retrieval substantially improve answer quality, underscoring the value of modeling the structural and temporal properties of 5G standards.

翻译：5G技术为数十亿用户提供了移动互联网接入服务。回答关于5G技术规范的专家级问题，需要查阅数千页跨版本演进且相互引用的标准文档。现有的检索增强生成框架（包括电信领域的专用方法）依赖于语义相似性，无法可靠地解析交叉引用或推理规范演进过程。本文提出DeepSpecs，这是一个通过三个元数据丰富的数据库（SpecDB：条款对齐的规范文本；ChangeDB：行级版本差异；TDocDB：标准化会议文档）进行结构和时序推理增强的RAG系统。DeepSpecs通过元数据查找递归检索被引用的条款，从而显式解析交叉引用；并通过挖掘变更内容并将其链接至记录设计原理的变更请求文档，追踪规范演进轨迹。我们构建了两个5G问答数据集：一个包含来自从业者论坛和教育资源的573个专家标注的真实问题，另一个包含源自已批准变更请求的350个以演进为核心的问题。在多种大语言模型后端上，DeepSpecs均优于基础模型和当前最先进的电信RAG系统；消融实验证实，显式的交叉引用解析和演进感知检索显著提升了答案质量，这凸显了对5G标准结构性和时序性特征建模的重要价值。