mlsanityData to 7 Oct 2026

Progressive Searching for Retrieval in RAG

arXiv 2602.07297 ↗First posted 7 Feb 2026cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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Augmented Generation (RAG) is a promising technique for mitigating two key limitations of large language models (LLMs): outdated information and hallucinations.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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 16 references that are in the map. First posted on arXiv 7 Feb 2026.

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6 of 16 references.

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Same direction, newest first

  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May

Other references · 5

  1. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  2. 2018Approximate Nearest Neighbor Search in High Dimensionscited by 45
  3. 2016Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)cited by 44
  4. 2024Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmarkcited by 19
  5. 2024VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Searchcited by 10

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  1. 2024Optimizing Query Generation for Enhanced Document Retrieval in RAG
  2. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  5. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  6. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey