Entropy Production Bounds the Accuracy of Computation in Markov Networks

What the math gives to ML

The paper provides a transferable thermodynamic speed–accuracy mechanism: in a stochastic Markov computation, lag error cannot be reduced arbitrarily unless the network either dissipates more entropy or retains information in slowly relaxing modes. The relevant quantities are entropy-production rate, integrated output autocorrelation time, and the rate at which the desired output changes. A neural analogue is a stochastic recurrent or state-space model with an explicit dissipation estimate and memory-time monitor. The resulting regularizer predicts a measurable accuracy–energy–memory tradeoff rather than merely encouraging generic smoothness.