The field of data structures and algorithms is witnessing significant advancements, driven by the need for efficient and scalable solutions to manage and process large amounts of data. A key direction in this field is the development of innovative techniques for approximate nearest neighbor search, kernel density estimation, and text indexing. Researchers are exploring new approaches, such as graph neural networks and sublinear sketches, to improve the performance and accuracy of these fundamental problems. Notably, these advancements are enabling faster query times, reduced memory usage, and improved update efficiency. Some noteworthy papers in this area include: The paper on prefetching cache optimization using graph neural networks, which introduces a modular framework for modeling and predicting access patterns in graph-structured data. The paper on dynamically detecting and fixing hardness for efficient approximate nearest neighbor search, which proposes a metric to evaluate the quality of the graph structure around the query and divides the graph search into two stages to dynamically identify and fix defective graph regions. The paper on sublinear sketches for approximate nearest neighbor and kernel density estimation, which develops new sketching algorithms that achieve sublinear space and query time guarantees for both problems. The paper on decoupled on-disk graph-based ANN index for efficient updates and queries, which proposes a decoupled storage architecture and designs two tailored strategies to reduce I/O and computational overhead during ANN search. The paper on space-efficient k-mismatch text indexes, which presents the first improvement to the original time-space trade-off for k-mismatch indexing in the general case.