Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.

Sampling in discrete spaces, with critical applications in simulation and optimization, has recently been boosted by significant advances in gradient-based approaches that exploit modern accelerators like GPUs. However, two key challenges are hindering further advancement in research on discrete sampling.

Bayesian brain theory suggests that the brain employs generative models to understand the external world.