Long-lived memory in sliding spin chains

What the math gives to ML

The paper identifies a concrete nonequilibrium memory mechanism: relative sliding of two ferromagnetic Ising chains creates magnetic friction that suppresses thermal loss of the initial magnetization. Its transferable asset is a velocity-controlled memory lifetime that changes scaling above a small critical speed, from equilibrium Arrhenius behavior to a parametrically slower thermalization law. A neural analogue is a coupled pair of recurrent or state-space latent chains with a controlled relative shift, where the shift rate becomes a learnable or scheduled memory-preservation parameter.