The field of table understanding and reasoning is rapidly advancing, with a focus on developing innovative methods to improve the accuracy and reliability of table-based question answering and fact verification. Recent research has explored the use of large language models (LLMs) to enhance table understanding, including the development of novel frameworks and techniques such as entity-oriented search, structured decoding, and long-term planning. These approaches have shown significant improvements in performance on various benchmarks, including WikiTableQuestions and TabFact. Additionally, there is a growing interest in multilingual table understanding, with the introduction of new benchmarks and evaluation metrics. The development of specialized tools and frameworks, such as ST-Raptor and InReAcTable, is also facilitating the automation of table-based tasks and the construction of visual data stories. Noteworthy papers include M3TQA, which introduces a comprehensive framework for massively multilingual multitask table question answering, and ST-Raptor, which proposes a tree-based framework for semi-structured table question answering using LLMs. Overall, the field is moving towards more sophisticated and generalizable methods for table understanding and reasoning, with a focus on real-world applications and industrial scenarios.