What makes an AI system good at math? Not raw computational power, but something that seems almost contradictory: being neurotically careful about being right.
When AI researchers talk about mathematical reasoning, they typically focus on scaling up – bigger models, more parameters, larger datasets. But in practice, mathematical ability isn’t about how much compute you have for your model. It’s actually about whether machines can learn to verify their own work, because at least 90% of reasoning errors come from models confidently stating wrong intermediate steps.
I guess this sounds obvious once you understand it. Any mathematician would tell you that the key to solving hard problems isn’t raw intelligence – it’s methodical verification. Yet for years, AI researchers have been trying to brute-force mathematical ability by making models bigger, as if sheer computational power alone would produce careful reasoning.
