SEFRQO-Plus: A Self-Evolving Query Optimizer via Large Language Models and Retrieval-Augmented Generation
This paper designs a feedback-oriented vector database with query structure-aware embed-dings to support effective similarity search, and incorporates multi-layered histories as references to enrich the feedback, and enhances the prompt optimization work-flow by utilizing multi-dimensional historical feedback to drive continuous self-evolution.
Han-Wen Liu, Qihan Zhang, Ryan Marcus et al.
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