Frontier AI models have pushed the boundaries of machine learning and artificial intelligence research and sparked transformative technological innovation in many US industries. These large-scale AI models are able to process and generate text, image, audio, and video, and currently require massive amounts of data and computing. This Mathematical Foundations of Artificial Intelligence (MFAI) project aims to uncover the mathematical principles that explain when and why these highly advanced AI models are so effective, and to overcome the fundamental limits of brute-force scale presently employed to surpass human expert intelligence in benchmarks. The project will advance the capabilities of AI models to conduct inference in new situations in which there is no training data, and to perform complex reasoning and problem-solving tasks. This research will ensure that the US remains the global leader in AI, advancing economic prosperity, national security, and global competitiveness. This project aims to rigorously characterize the mathematical frontiers of generative AI models, including state-of-the-art large language models (LLMs), by developing new theoretical frameworks and modeling principles rooted in machine learning, probability theory, variational analysis, mathematical statistics, and information theory. The research will investigate how frontier AI models achieve remarkable performance despite fundamental theoretical barriers and will identify the key mathematical qu