The field of dimensionality reduction and similarity search is moving towards more efficient and effective methods for handling high-dimensional data. Recent developments have focused on improving the accuracy and scalability of algorithms for tasks such as maximum inner product search, nearest neighbor search, and dimensionality reduction. Notably, there is a growing trend towards combining different metrics and approaches, such as inner product and Euclidean metrics, to achieve better performance. Additionally, researchers are exploring new techniques for preserving the geometry of the original data during dimensionality reduction, and for creating parametric and invertible projections. Overall, these advances have the potential to enable faster and more accurate search and analysis of high-dimensional data. Noteworthy papers include: Training Autoencoders Using Stochastic Hessian-Free Optimization with LSMR, which accelerates the training of deep autoencoders. RT-HDIST: Ray-Tracing Core-based Hausdorff Distance Computation, which achieves significant reductions in computational overhead for Hausdorff distance computation. MPAD: A New Dimension-Reduction Method for Preserving Nearest Neighbors in High-Dimensional Vector Search, which preserves approximate nearest-neighbor relations in high-dimensional vector search.