The field of search and dimensionality reduction is moving towards more efficient and scalable solutions. Recent developments have focused on improving the performance of approximate nearest neighbor search (ANNS) algorithms, which are crucial in various applications such as recommendation systems and image retrieval. Notably, researchers have proposed novel data structures and algorithmic methods to reduce the dimensionality of sparse vectors while preserving their inner product-induced ranks, leading to significant improvements in query efficiency. Additionally, there has been progress in designing compact integer linear programs (ILPs) for parameterized problems, which can be solved more efficiently in practice. Furthermore, neural network-based dimensionality reduction methods have been introduced, which can preserve the nearest neighbor relationship among vectors. Overall, these advancements have the potential to significantly impact the field of search and dimensionality reduction, enabling faster and more accurate querying of large datasets. Noteworthy papers include: Beyond Johnson-Lindenstrauss, which develops a general framework to analyze sketched bilinear forms and derive uniform bounds. Efficient Sketching and Nearest Neighbor Search Algorithms for Sparse Vector Sets, which introduces a set of novel data structures and algorithmic methods for sparse ANNS. RAE: A Neural Network Dimensionality Reduction Method for Nearest Neighbors Preservation in Vector Search, which proposes a regularized auto-encoder for k-NN preserving dimensionality reduction. Panorama: Fast-Track Nearest Neighbors, which presents a machine learning-driven approach to tackle the ANNS verification bottleneck.