The field of database systems is witnessing significant advancements in query optimization, with a focus on improving performance, scalability, and integration with artificial intelligence and machine learning models. Researchers are exploring novel approaches to optimize complex graph queries, estimate execution costs for user-defined functions, and develop virtual indexes that can be easily integrated with cloud-native environments and AI-driven models. Additionally, there is a growing interest in leveraging execution results to guide SQL generation and integrating large language models with database systems to support knowledge-intensive analytical applications. These innovations have the potential to significantly improve the efficiency and effectiveness of database systems, enabling them to handle complex queries and large datasets with ease. Noteworthy papers include: GRACEFUL, which introduces a learned cost estimator for user-defined functions, and FlockMTL, which integrates large language models with database systems to support knowledge-intensive analytical applications.