Physics Department - Information Swimmers

Abstract
We introduce the informational Onsager-Machlup principle (IOMP), a generalized variational framework that unifies energetic, dissipative, and informational contributions within a single formalism.  We define the conditioned Onsager-Machlup integral to quantify path entropy under a given memory state and derive the cumulant generating function for arbitrary observables in systems with measurement and feedback.  We apply the IOMP to a minimal model information swimmer, where feedback adaptively modulates viscous drag based on noisy velocity measurements. We provide an analytical expression for the average swimming velocity and higher-order cumulants.