5G technology enables mobile Internet access for billions of users. Its design, implementation and operations are regulated by 3GPP standard specifications. We study standard-native question answering over 5G specifications, where expert-level queries require navigating thousands of pages of cross-referenced standards that evolve across tens of 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 standard-native RAG system with three metadata-rich indices: SpecDB (clause-aligned specification text), ChangeDB (line-level version diffs), and TDocDB (Change Requests with design rationale). DeepSpecs resolves cross-references by recursively retrieving referenced clauses via metadata lookup, and traces evolution by mining clause changes and linking them to corresponding Change Requests. We curate two 5G QA datasets: 573 expert-annotated real-world questions 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 cross-reference resolution and evolution-aware retrieval substantially improve answer quality. Our methodology is conceptually applicable to other networked systems.