Optimal-work feedback on particles with activity --- gliding on active fluctuations using positional information

What the math gives to ML

The paper provides a nontrivial partial-observation control mechanism: an active Ornstein–Uhlenbeck degree of freedom is hidden, but its correlation with the measured particle position allows work extraction and near-optimal feedback using position alone. The transferable asset is a controller that infers persistent latent activity from observable state histories rather than measuring the disturbance directly. In neural-network optimization, parameters or activations can be modeled as trapped states driven by colored gradient noise, and a Kalman-like estimator can separate predictable activity from irreducible stochastic noise before applying a feedback update. The key falsifiable prediction is that the benefit depends sharply on gradient-noise persistence time and remains nonzero for very large persistence when latent activity is inferred from parameter or gradient history.