The field of information access and knowledge representation is undergoing significant developments, driven by the need for more personalized and dynamic systems. Researchers are exploring innovative approaches to improve the accuracy and diversity of search results, such as user-centric calibration and stigmergy-based algorithms. Additionally, there is a growing focus on leveraging large language models and artificial intelligence to enhance knowledge representation, taxonomy alignment, and semantic embedding. These advancements have the potential to transform the way we access and interact with information, enabling more efficient and effective knowledge discovery. Noteworthy papers include LLM-BT, which proposes a back-translation framework for terminology standardization and dynamic semantic embedding, and TaxoAdapt, which introduces a framework for aligning LLM-based multidimensional taxonomy construction to evolving research corpora. These works demonstrate significant progress towards creating more robust and adaptive information systems.