The system has two especially novel features. First, the neural net is trained only on algorithmically generated data — a whopping 100 million geometric proofs — using no human examples.

Instead of using natural language, Trinh and his collaborators developed a language for writing geometry proofs that has a rigid syntax similar to that of a computer programming language.