AI for mathematics

Mathematics is one of the few domains where correctness is checkable: a proof either holds or it doesn't. That verifiability makes it a natural fit for machine learning — the model generates candidates, a checker filters them, and successful moves become training signal. In practice this means proof assistants guided by neural search, language models trained on formal libraries, and reinforcement learning applied to competition problems. The hard part is the combinatorial explosion: the space of possible proof steps is enormous and most paths are dead ends. When a model finds a path humans missed, the gap is often not cleverness but patience — it can explore more of the tree.