Definition
A math safety net that stops fixed-point training from drifting into unstable regions, because the gradient itself blows up if it tries.
In Attractor Models, the observation that the implicit gradient involves an inverse of one-minus-Jacobian which diverges as the refinement step loses contractivity, structurally confining gradient descent to convergent regimes.