Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on past observations and navigation actions. Temporally- and spatially-consistent, long-term world modeling has been a long-standing problem, unresolved with even recent state-of-the-art models, due to the prohibitively expensive computational costs for long-context inputs. In this paper, we propose WorldPack, a video world model with efficient compressed memory, which significantly improves spatial consistency, fidelity, and quality in long-term generation despite much shorter context length. Our compressed memory consists of trajectory packing and memory retrieval; trajectory packing realizes high context efficiency, and memory retrieval maintains the consistency in rollouts and helps long-term generations that require spatial reasoning. Our performance is evaluated with LoopNav, a benchmark on Minecraft, specialized for the evaluation of long-term consistency, and we verify that WorldPack notably outperforms strong state-of-the-art models.
@article{arxiv.2512.02473,
title = {WorldPack: Compressed Memory Improves Spatial Consistency in Video World Modeling},
author = {Yuta Oshima and Yusuke Iwasawa and Masahiro Suzuki and Yutaka Matsuo and Hiroki Furuta},
journal= {arXiv preprint arXiv:2512.02473},
year = {2025}
}