This paper introduces TTT-Discover, an innovative system designed to solve complex science and engineering problems through test-time training. Unlike traditional static models, this approach enables an open-source AI to continuously learn and refine its policy while actively seeking solutions for a specific task. By utilizing an entropic objective and adaptive reinforcement learning, the system successfully established new state-of-the-art results in mathematics, GPU kernel engineering, and biology. The researchers demonstrate that this method can outperform elite human experts and powerful closed-frontier models at a fraction of the typical computational cost. This framework effectively transforms the problem-solving process into an iterative search and learning environment where the model improves itself until a breakthrough is reached. Notably, the paper details successful applications in Erdős’ minimum overlap problem and high-performance algorithm design.