State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems from suboptimal inference strategies. We therefore introduce WMReward and treat improving physics plausibility of video generation as an inference-time alignment problem. In particular, we leverage the strong physics prior of a latent world model (here, VJEPA-2) as a reward to search and steer multiple candidate denoising trajectories, enabling scaling test-time compute for better generation performance. Empirically, our approach substantially improves physics plausibility across image-conditioned, multiframe-conditioned, and text-conditioned generation settings, with validation from human preference study. Notably, in the ICCV 2025 Perception Test PhysicsIQ Challenge, we achieve a final score of 62.64%, winning first place and outperforming the previous state of the art by 7.42%. Our work demonstrates the viability of using latent world models to improve physics plausibility of video generation, beyond this specific instantiation or parameterization.
@article{arxiv.2601.10553,
title = {Inference-time Physics Alignment of Video Generative Models with Latent World Models},
author = {Jianhao Yuan and Xiaofeng Zhang and Felix Friedrich and Nicolas Beltran-Velez and Melissa Hall and Reyhane Askari-Hemmat and Xiaochuang Han and Nicolas Ballas and Michal Drozdzal and Adriana Romero-Soriano},
journal= {arXiv preprint arXiv:2601.10553},
year = {2026}
}