go-mHC enables efficient learned mixing of residual streams in transformers with a single tunable hyperparameter that trades off between speed and expressivity, potentially unlocking a new dimension for scaling model capacity.
This paper solves a mathematical problem in neural network design: how to efficiently mix information across different processing paths (residual streams) in transformers.