The recent advancements in the field of mathematical reasoning and numerical cognition have shown significant progress, particularly in leveraging language models to tackle complex mathematical problems. Researchers are increasingly focusing on understanding and enhancing the numerical capabilities of large language models (LLMs), with a particular emphasis on how these models represent and process numbers. Innovations in this area include the development of models that can accurately handle numerical data in tabular formats and the exploration of how linguistic structures influence numerical learning in reinforcement learning agents. Additionally, there is a growing interest in improving the reliability and accuracy of LLMs in mathematical reasoning tasks, including the ability to recognize and abstain from unanswerable problems. Notably, advancements in numerical precision have been identified as critical for enhancing the mathematical reasoning capabilities of LLMs, with studies showing that precision levels directly impact the model's ability to perform arithmetic tasks. These developments collectively point towards a future where LLMs can be more effectively integrated into mathematical problem-solving and educational applications, offering new insights and tools for both human learners and artificial intelligence systems.