The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI
Jiuqi Wei, Quanqing Xu, Chuanhui Yang

TL;DR
This paper reviews the emerging synergy between AI and vector search, highlighting how each enhances the other, especially through Retrieval-Augmented Generation, and discusses future research directions in this rapidly evolving field.
Contribution
It provides a comprehensive overview of recent advancements and co-optimization strategies at the intersection of AI and vector search, emphasizing the virtuous cycle and future challenges.
Findings
AI improves vector search accuracy and efficiency
Vector search enables advanced AI paradigms like RAG
End-to-end co-optimization enhances system performance
Abstract
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…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Topic Modeling · Multimodal Machine Learning Applications
