The "free-energy principle" in this case isn't just variational inference via a free-energy cost derived from the KL divergence. It involves treating action (c

The "free-energy principle" in this case isn't just variational inference via a free-energy cost derived from the KL divergence. It involves treating action (control signals) as a variational parameter to the recognition density. The agent thus treats their likelihood function as a descriptive model of the world, and their prior as a prescriptive model: they update their beliefs (recognition density) to be accurate Bayesian inferences, but then act so as to reduce prior improbability (despite accumulation of likelihood data).