At 50 million tokens, the design space for AI applications changes fundamentally.

Parameters are estimated by unweighted least squares. Time t is measured in years since the first observation in each dataset.

Without any architectural modification, MinerU2.5-Pro achieves 95.69 on OmniDocBench v1.6, improving over the same-architecture baseline by 2.71 points and surpassing all existing methods including models with over 200× more parameters.

how the features are all on the same relative scale. The relative spaces between each feature’s values have been maintained.

However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.