The field of mathematical reasoning and AI is moving towards developing more advanced and efficient models for solving complex mathematical problems. Researchers are exploring new methods for integrating AI systems with mathematical research, such as using multi-agent frameworks and distillation techniques to improve structured reasoning. The development of large-scale datasets and the application of FAIR principles to mathematical research data are also significant trends in this area. These advancements have the potential to significantly improve the performance of AI systems in mathematical reasoning tasks and enhance their ability to assist mathematical research. Noteworthy papers include: The AIMO-2 Winning Solution paper, which presents a novel approach to building state-of-the-art mathematical reasoning models using a large-scale dataset and a pipeline to train models to select the most promising solution. The DeepDistill paper, which constructs a large-scale, difficulty-graded reasoning dataset and uses it to enhance the reasoning capabilities of large language models, achieving state-of-the-art results on mathematical reasoning benchmarks.