The field of database management is witnessing significant advancements in SQL-aided table understanding and query optimization. Recent developments have focused on leveraging large language models (LLMs) to improve the accuracy and efficiency of query generation and optimization. Multi-agent frameworks and lifelong learning approaches are being explored to address the challenges of understanding tabular data and generating accurate SQL queries. Additionally, there is a growing interest in self-evolving query optimizers that can learn from execution feedback and adapt to changing workloads. These advancements have the potential to significantly improve the performance and scalability of database systems. Noteworthy papers include: Chain-of-Query, which proposes a novel multi-agent framework for SQL-aided table understanding, and RubikSQL, which presents a lifelong learning agentic knowledge base for industrial NL2SQL systems. SEFRQO, a self-evolving fine-tuned RAG-based query optimizer, is also noteworthy for its ability to mitigate the cold-start problem and improve query performance.