Light-induced nonconservative static forces in many-body systems

What the math gives to ML

The paper identifies a transferable mechanism: periodic driving plus dissipation produces a static force with a genuinely nonconservative component, characterized by nonzero circulation around closed loops in collective-coordinate space. In neural-network training, this suggests augmenting gradient descent with a controlled rotational force in parameter space, rather than assuming every useful update is the gradient of a scalar objective. The rotational component can transport parameters along loss valleys and between basins while a symmetric dissipative component controls stability. The key falsifiable signature is nonzero measured loop work together with a sharp stability boundary determined by the spectral radius of the resulting update map.