The field of Text-to-SQL research is rapidly advancing, with a focus on improving the accuracy and robustness of models that can translate natural language queries into SQL executions. Recent developments have seen the introduction of new frameworks, benchmarks, and techniques that are pushing the boundaries of what is possible in this area. One key trend is the use of reinforcement learning and test-time scaling to improve model performance, with some models achieving state-of-the-art results on challenging benchmarks like BIRD. Another area of focus is the development of multilingual Text-to-SQL models, which can handle queries in multiple languages and are robust across different languages and databases. The creation of new benchmarks and evaluation frameworks, such as GeoSQL-Eval and DeepJSONEval, is also enabling more comprehensive assessments of model performance and driving progress in the field. Notable papers in this area include Agentar-Scale-SQL, which achieved SOTA performance on the BIRD benchmark, and SING-SQL, which introduced a novel framework for generating high-quality synthetic Text-to-SQL data. Additionally, Thinkquel presented a fine-tuned model for producing robust and portable database queries, and Exploring Database Normalization Effects on SQL Generation highlighted the importance of considering schema design when developing NL2SQL interfaces.