467655d26fcc207bca08915dc91964c6-Paper-Conference.pdf

World models are generative systems that learn to predict an environment in response to actions, making them well suited for simulating complex, interactive settings [28, 2, 30, 74, 90]. Video diffusion models [11, 37, 44, 79, 55] have emerged as a powerful approach to architecting world models, especially when used with autoregressive next-frame prediction [1, 12, 18, 22, 41, 53, 60, 65, 73, 81, 35]. Existing video generation models, however, often struggle with long-horizon consistency due to limited temporal context windows, frequently forgetting previously seen scenes during revisits. This is due to the relatively small number of previously generated context frames that the model can consider when generating new frames--a problem primarily caused by the quadratic growth of computational complexity in the attention module of the underlying diffusion transformers. To address this challenge, current world models simply keep the number of context frames low to maintain computational feasibility.