We propose a new algorithm that introduces guarantees for minimum user satisfaction rates in language model zoos while optimizing for operating cost, which can be practical for inference endpoint services.

We propose EVODiff, a novel information-theoretically grounded framework that optimizes conditional variance in diffusion models' generative process, achieving significant gains in both efficiency and image quality without prior trajectories.