Norm-Agnostic Residual Networks (NAG)

"Scaling Adaptive Depth with Norm-Agnostic Residual Networks": splits the residual stream into norm and direction components to build an interpretable Mixture-of-Depths, matching baseline quality while skipping 20–25% of layer compute. Architecture-efficiency research in the vein of Zyphra's hybrid-model identity.