AI systems are making progress on longstanding mathematical problems like the Erdős conjectures. Here's what's happening, what's working, and where the limits still lie.
The AI field spent two years proving that longer reasoning chains produce better answers. Now a wave of new research shows the opposite: beyond a certain point, more 'thinking' actively degrades performance. The overthinking problem is real, measurable, and it's forcing a rethink of how we deploy reasoning models.
Pre-training scale isn't the only lever anymore. Test-time compute scaling lets models think longer at inference — and the results are reshaping how we measure AI capability.