Min-Soo Kim

CONDA: A Connectivity-Aware Dynamic Index for Approximate Nearest Neighbor Search over Evolving Data

Graph-based Approximate Nearest Neighbor Search (ANNS) indices must support continuous vector updates while maintaining both high search performance and low update overhead for real-world applications such as RAG systems and streaming services. Existing in-place update methods often lose search accuracy due to graph connectivity loss and suffer high overhead from expensive deletion operations. We propose CONDA, a dynamic graph-based index featuring a topology-aware pruning rule with bidirectional link reinforcement and a lightweight lazy deletion scheme. Extensive experiments demonstrate that CONDA improves search recall by up to 24.5% over state-of-the-art methods while achieving 1.90× higher update throughput.

SafeQL: Search-based Refinement for Safe and E � icient LLM-based Text-to-SQL

SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL

SafeQL is proposed, a search-based refinement paradigm that redefines the role of the DBMS as an active guide in the refinement process, and significantly improves execution accuracy and efficiency compared to regeneration-based methods.