#testtimecompute

A new analysis shows that sampling many answers from an AI and picking the most common one hits a hard ceiling because the samples are correlated, not independent, so thousands of extra tries can be worth only a couple of genuinely new ones.

Controlled experiments on constraint satisfaction problems. MCTS beats best-of-N only when blind sampling hits a ceiling and the verifier provides a gradient.