"Contrastive World Models" If world model has to reconstruct every pixel, it'll pretty much waste most of its capacity modeling irrelevant background noise. So this paper removes Google DeepMind's Dreamer pixel decoder and instead trains the latent state to identify features of the correct future observation. It matches Dreamer on clean environments, but performs much better with moving distractors and natural-video backgrounds. https://alphaxiv.org/abs/2609.22175