Definition
Running a neural network's layers multiple times over the same input to get more computation out of the same weights.
An architectural pattern that loops a stack of blocks repeatedly during a forward pass, trading sequential compute for additional reasoning capacity at fixed parameter count; instantiated in Universal Transformers, looped transformers, and attractor-style models.
Also called: recurrent depth