Progressive Searching for Retrieval in RAG

• URL: http://arxiv.org/abs/2602.07297v1
• Date: Sat, 07 Feb 2026 01:12:53 GMT
• Title: Progressive Searching for Retrieval in RAG
• Authors: Taehee Jeong, Xingzhe Zhao, Peizu Li, Markus Valvur, Weihua Zhao,
• Abstract summary: Retrieval Augmented Generation (RAG) is a promising technique for mitigating two key limitations of large language models (LLMs): outdated information and hallucinations.<n>We propose a cost-effective searching algorithm for retrieval process.<n>Our findings demonstrate that progressive search in RAG systems achieves a balance between dimensionality, speed, and accuracy, enabling scalable and high-performance retrieval even for large databases.
• Score: 1.1912082737504726
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval Augmented Generation (RAG) is a promising technique for mitigating two key limitations of large language models (LLMs): outdated information and hallucinations. RAG system stores documents as embedding vectors in a database. Given a query, search is executed to find the most related documents. Then, the topmost matching documents are inserted into LLMs' prompt to generate a response. Efficient and accurate searching is critical for RAG to get relevant information. We propose a cost-effective searching algorithm for retrieval process. Our progressive searching algorithm incrementally refines the candidate set through a hierarchy of searches, starting from low-dimensional embeddings and progressing into a higher, target-dimensionality. This multi-stage approach reduces retrieval time while preserving the desired accuracy. Our findings demonstrate that progressive search in RAG systems achieves a balance between dimensionality, speed, and accuracy, enabling scalable and high-performance retrieval even for large databases.

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