Definition
A model design that finds an answer by jumping straight to where its internal computation would settle, instead of iterating to get there.
An architecture that frames recurrent refinement as a fixed-point problem and uses implicit differentiation for constant-memory training, with the equilibrium living in output-embedding space.
Also called: Attractor Models