The field of data structures and algorithms is witnessing significant advancements, driven by the need for efficient solutions to modern applications. Researchers are focusing on developing innovative data structures and algorithms that can handle large datasets, reduce computational complexity, and improve performance. One notable direction is the development of compressed data structures, such as the LZD+ and LZDR compression schemes, which enable fast and efficient compression of repetitive datasets. Another area of research is the design of efficient indexing data structures, including the BS-tree and BMTree, which offer improved query performance and robustness. Additionally, researchers are exploring new methods for pruning large language models, such as the Shapley Value-based Non-Uniform Pruning and ReplaceMe, which aim to reduce model sizes while preserving performance. Noteworthy papers in this area include the introduction of zip-tries, a simple and dynamic data structure for strings, and the development of SPAP, a novel structured pruning framework for large language models. These advances have the potential to significantly impact various fields, including natural language processing, computer networks, and databases.