Run-and-tumble particles with preferred reorientation

What the math gives to ML

The paper provides a constructive reduction of arbitrary non-uniform reorientation dynamics to an effective persistence-and-chirality process controlled by the first Fourier coefficient of the tumble distribution. Its transferable asset is a principled way to parameterize recurrent state transitions as damped rotations, rather than learning unconstrained recurrent dynamics. This suggests a stable oscillatory RNN or state-space layer with interpretable, learnable memory and an explicit spectral stability condition.