The field of database query processing is witnessing significant advancements with the integration of large language models (LLMs) and innovative optimization techniques. Recent developments focus on improving the accuracy and efficiency of text-to-SQL systems, leveraging techniques such as guided error correction, outcome reward models, and self-play fine-tuning. Additionally, there is a growing emphasis on context-aware query plan optimization, leveraging listwise learning-to-rank and robust hybrid decision mechanisms. Noteworthy papers include SQL-of-Thought, which proposes a multi-agent framework for text-to-SQL systems, and CARPO, which introduces a generic framework for context-aware query plan optimization. Other notable works include GradeSQL, which evaluates outcome reward models for ranking SQL queries, and X-SQL, which proposes a novel database schema expert for text-to-SQL tasks. These advancements have the potential to significantly improve the performance and reliability of database query processing systems.