LLN: Learnable Lens Networks for Parameter-Efficient Long-Horizon Dynamical Prediction

Explicit residual connections of the form (x+f(x)), often combined with normalization layers, have become a standard strategy for training very deep neural networks. However, residual addition primarily provides an algebraic shortcut for gradient propagation, while leaving the evolution of feature g