Recent advancements in Retrieval-Augmented Generation have highlighted persistent challenges with temporal and cross-document queries, prompting a structural debate between two emerging architectural paradigms: LLM-Wiki and Proxy-Pointer. While traditional RAG efficiently handles isolated document queries, it struggles when tracking entity histories across multiple sources or time periods. In response, developers have proposed competing methods to resolve this scalability and reasoning bottleneck. The LLM-Wiki framework addresses temporal reasoning through eager semantic precompilation. During document ingestion, large language models extract entities, relationships, and facts, consolidating them into a persistent, canonical knowledge base linked by an index. This architecture transforms query responses into rapid lookups rather than real-time analysis. However, this approach introduces significant operational trade-offs. The system demands high upfront computational costs and forces designers to predict future query patterns during ingestion. As extraction objectives multiply, language models experience attention degradation over long documents, increasing the risk of missed information. Furthermore, maintaining a continuously evolving compiled knowledge base requires rigorous validation, complicating long-term scalability and explainability. In contrast, the Proxy-Pointer architecture adopts a lazy, structure-aware retrieval model that defers semantic analysis until query execution. Instead of preemptively compiling facts, Proxy-Pointer constructs a lightweight structural skeleton of each document using regex-based parsing. Chunks are segmented strictly within section boundaries and tagged with precise metadata breadcrumbs, such as publication year and subsection. When a temporal query arrives, the system leverages vector search and a language model re-ranker to isolate only the most relevant structural segments across multiple documents. This just-in-time synthesis eliminates upfront language model ingestion costs and reduces semantic noise by analyzing only query-specific content. The architectural divergence yields distinct operational outcomes. Proxy-Pointer delivers superior cost efficiency for large, evolving enterprise repositories where temporal queries represent a small fraction of overall usage. By processing only necessary sections at query time, the system avoids the storage and maintenance overhead of comprehensive knowledge bases. The approach also enhances explainability, as reasoning traces directly back to original source sections, satisfying regulatory compliance requirements. While LLM-Wiki excels in environments with stable corpora and highly repetitive conceptual queries, Proxy-Pointer is optimized for unpredictable information retrieval and complex historical analysis. Repeated queries can be cached to amortize computational expenses, preserving the efficiency of lazy evaluation. Developed as a fully open-source framework, Proxy-Pointer represents a strategic shift from predictive knowledge compilation to on-demand semantic synthesis. The architecture challenges enterprise artificial intelligence developers to reassess the balance between upfront indexing costs and real-time reasoning overhead. As corporate data repositories expand into millions of pages, deferring semantic processing to the point of retrieval offers a scalable pathway for handling temporal complexity without compromising accuracy or compliance. Organizations prioritizing agile, cost-effective retrieval deployments for dynamic documentation are increasingly adopting this retrieval-first paradigm over traditional ingestion-heavy models.