Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out-of-distribution and long-tail scenarios. Instead of requiring users to select suitable methods through extensive experimentation, we attempt to enable systems to adaptively construct workflows at inference time. Through theoretical and empirical analysis, we demonstrate that optimal dynamic policies consistently outperform the best static workflow, with performance gains fundamentally driven by heterogeneity across candidate workflows. Motivated by this, we propose SquRL, a reinforcement learning framework that enhances LLMs' reasoning capability in adaptive workflow construction. We design a rule-based reward function and introduce two effective training mechanisms: dynamic actor masking to encourage broader exploration, and pseudo rewards to improve training efficiency. Experiments on widely-used Text-to-SQL benchmarks demonstrate that dynamic workflow construction consistently outperforms the best static workflow methods, with especially pronounced gains on complex and out-of-distribution queries. The codes are available at https://github.com/Satissss/SquRL
BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
Center for Computational & Data Sciences, Independent University, Bangladesh（计算与数据科学中心，独立大学，孟加拉国）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；HSBC（汇丰银行）；South China University of Technology（华南理工大学）；Harvard University（哈佛大学）；Chicago University（芝加哥大学）
Are These Modules Worth Their Cost? A Paradigm-Level Accuracy-Cost Analysis of In-context Learning Text-to-SQL
Jinan University（暨南大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Macau（澳门城市大学）；Jilin University（吉林大学）；Beihang University（北京航空航天大学）
Query Carefully: Detecting the Unanswerables in Text-to-SQL Tasks
Institute of Computer Science, Zurich University of Applied Sciences（计算机科学研究所，应用科学大学苏黎世）；Institute of Medical Virology, University of Zurich（医学病毒学研究所，苏黎世大学）；Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich（消化内科与肝病科，苏黎世大学医院，苏黎世大学）
School of Information, Renmin University of China（中国人民大学信息学院）；Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, China（数据工程与知识工程重点实验室）；Engineering Research Center of Database and Business Intelligence, Beijing, China（数据库与商业智能工程研究中心）；Ant Group, Hangzhou, China（蚂蚁集团）
ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQL
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；College of Architecture and Urban Planning, Tongji University（同济大学建筑与城市规划学院）；Behavioral and Spatial AI Lab, Peking University & Tongji University（北京大学与同济大学行为与空间人工智能实验室）
CORE-T: COherent REtrieval of Tables for Text-to-SQL
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany（普适知识处理实验室（UKP实验室），计算机科学系 TU Darmstadt 和应用网络安全国家研究中心 ATHENE，德国）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
University of Hong Kong（香港大学）；Salesforce Research（Salesforce 研究院）；Sea AI Lab（Sea AI 实验室）；Google Deepmind（谷歌DeepMind）；Google Cloud AI Research（谷歌云 AI 研究院）；University of Waterloo（滑铁卢大学）
Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
Dhillon School of Business, University of Lethbridge（莱斯布里奇大学迪隆商学院）；Universidad de Guadalajara（瓜达拉哈拉大学）；Al Hussein Technical University（侯赛因技术大学）；Universidad de Guanajuato（瓜纳华托大学）
