mlsanityData to 7 Oct 2026

The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI

arXiv 2603.09347 ↗First posted 10 Mar 2026cs.DBcited by 0

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

Abstract (arXiv)

Modern AI and vector search are rapidly converging, forming a promising research frontier in intelligent information systems. On one hand, advances in AI have substantially improved the semantic accuracy and efficiency of vector search, including learned indexing structures, adaptive pruning strategies, and automated parameter tuning. On the other hand, powerful vector search techniques have enabled new AI paradigms, notably Retrieval-Augmented Generation (RAG), which effectively mitigates challenges in Large Language Models (LLMs) like knowledge staleness and hallucinations. This mutual reinforcement establishes a virtuous cycle where AI injects intelligence and adaptive optimization into vector search, while vector search, in turn, expands AI's capabilities in knowledge integration and context-aware generation. This tutorial provides a comprehensive overview of recent research and advancements at this intersection. We begin by discussing the foundational background and motivations for integrating vector search and AI. Subsequently, we explore how AI empowers vector search (AI4VS) across each step of the vector search pipeline. We then investigate how vector search empowers AI (VS4AI), with a particular focus on RAG frameworks that integrate dynamic, external knowledge sources into the generative process of LLMs. Furthermore, we analyze end-to-end co-optimization strategies that fully unlock the potential of the ``virtuous cycle" between vector search and AI. Finally, we highlight key challenges and future research opportunities in this emerging area. This paper was published in ICDE 2026.

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 61 of its 114 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

61 of 114 references.

Closest ancestors · 7

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  1. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions25 Jul
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May
  3. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers22 Mar
  4. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  5. 2024RAG-Fusion: a New Take on Retrieval-Augmented Generation31 Jan
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  7. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory3 May

Other references · 54

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2023Qwen Technical Reportcited by 3,273
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  11. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  12. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
38 more
  1. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
  2. 2024The Faiss librarycited by 370
  3. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  4. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  5. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  6. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  7. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  8. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 150
  9. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  10. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 123
  11. 2017Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graphcited by 103
  12. 2021A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Searchcited by 82
  13. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  14. 2023Large Language Model based Long-tail Query Rewriting in Taobao Searchcited by 61
  15. 2023Query Expansion by Prompting Large Language Modelscited by 56
  16. 2023KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Basescited by 52
  17. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 49
  18. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  19. 2024RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Searchcited by 46
  20. 2015Simultaneous Feature Learning and Hash Coding with Deep Neural Networkscited by 44
  21. 2017Asymmetric Deep Supervised Hashingcited by 35
  22. 2024MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodingscited by 30
  23. 2024Context Embeddings for Efficient Answer Generation in RAGcited by 26
  24. 2025AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generationcited by 19
  25. 2023From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICLcited by 17
  26. 2025Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generationcited by 12
  27. 2022DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queriescited by 10
  28. 2019Learning to Route in Similarity Graphscited by 9
  29. 2023Crosslingual Retrieval Augmented In-context Learning for Banglacited by 7
  30. 2024DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Searchcited by 6
  31. 2024SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Searchcited by 6
  32. 2024VDTuner: Automated Performance Tuning for Vector Data Management Systemscited by 5
  33. 2024Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Searchcited by 4
  34. 2023Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classificationcited by 3
  35. 2025VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Searchcited by 3
  36. 2025LIRA: A Learning-based Query-aware Partition Framework for Large-scale ANN Searchcited by 3
  37. 2025DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Searchcited by 3
  38. 2025PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphscited by 3

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