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Search Is Not Retrieval: Decoupling Semantic Matching from Contextual Assembly in RAG

arXiv 2511.04939 ↗First posted 7 Nov 2025cs.IR cs.AIcited by 0

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Abstract (arXiv)

Retrieval systems are essential to contemporary AI pipelines, although most confuse two separate processes: finding relevant information and giving enough context for reasoning. We introduce the Search-Is-Not-Retrieve (SINR) framework, a dual-layer architecture that distinguishes between fine-grained search representations and coarse-grained retrieval contexts. SINR enhances the composability, scalability, and context fidelity of retrieval systems by directly connecting small, semantically accurate search chunks to larger, contextually complete retrieve chunks, all without incurring extra processing costs. This design changes retrieval from a passive step to an active one, making the system architecture more like how people process information. We discuss the SINR framework's conceptual foundation, formal structure, implementation issues, and qualitative outcomes. This provides a practical foundation for the next generation of AI systems that use retrieval.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 7 Nov 2025.

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References · 26

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14 more
  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
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  14. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directionscited by 69

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