Linear Attention as Bayesian Inference

This paper shows that the recurrent update used in linear attention models is mathematically equivalent to Bayesian inference under a Dependent Dirichlet Process. The forgetting rate, the input-dependent gate, and the multi-head structure each receive a clean probabilistic interpretation. The result is exact and holds by construction, not as an approximation.