PAN: A World Model for General, Interactable, and Long-Horizon World Simulation
A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual sequences, they typically operate in the prompt-to-full-video manner without causal control, interactivity, or long-horizon consistency required for purposeful reasoning. Existing world modeling efforts, on the other hand, often focus on restricted domains (e.g., physical, game, or 3D-scene dynamics) with limited depth and controllability, and struggle to generalize across diverse environments and interaction formats. In this work, we introduce PAN, a general, interactable, and long-horizon world model that predicts future world states through high-quality video simulation conditioned on history and natural language actions. PAN employs the Generative Latent Prediction (GLP) architecture that combines an autoregressive latent dynamics backbone based on a large language model (LLM), which grounds simulation in extensive text-based knowledge and enables conditioning on language-specified actions, with a video diffusion decoder that reconstructs perceptually detailed and temporally coherent visual observations, to achieve a unification between latent space reasoning (imagination) and realizable world dynamics (reality). Trained on large-scale video-action pairs spanning diverse domains, PAN supports open-domain, action-conditioned simulation with coherent, long-term dynamics. Extensive experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning compared to other video generators and world models, taking a step towards general world models that enable predictive simulation of future world states for reasoning and acting.
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PAN Team
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Jiannan Xiang
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Yi Gu
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Zihan Liu
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Zeyu Feng
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Qiyue Gao
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Yiyan Hu
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Yiyan Hu
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Benhao Huang
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Benhao Huang
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Guangyi Liu
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Guangyi Liu
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Yichi Yang
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Kun Zhou
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Arif Ahmad
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Arif Ahmad
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Junrong Chen
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Junrong Chen
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Kimi Chen
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Mingkai Deng
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Ruobing Han
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Xinqi Huang
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Haoqiang Kang
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Zheqi Li
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Enze Ma
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Hector Ren
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Kaiming Tao
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Dequan Yang
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Xinle Yu
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Cong Zeng
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Binglin Zhou
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Zhengzhong Liu
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Zhiting Hu
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Eric P. Xing
PAN: A World Model for General, Interactable, and Long-Horizon World Simulation
A world model enables an intelligent agent to imagine, predict, and reason about how the world evolves in response to its actions, and accordingly to plan and strategize. While recent video generation models produce realistic visual sequences, they typically operate in the prompt-to-full-video manner without causal control, interactivity, or long-horizon consistency required for purposeful reasoning. Existing world modeling efforts, on the other hand, often focus on restricted domains (e.g., physical, game, or 3D-scene dynamics) with limited depth and controllability, and struggle to generalize across diverse environments and interaction formats. In this work, we introduce PAN, a general, interactable, and long-horizon world model that predicts future world states through high-quality video simulation conditioned on history and natural language actions. PAN employs the Generative Latent Prediction (GLP) architecture that combines an autoregressive latent dynamics backbone based on a large language model (LLM), which grounds simulation in extensive text-based knowledge and enables conditioning on language-specified actions, with a video diffusion decoder that reconstructs perceptually detailed and temporally coherent visual observations, to achieve a unification between latent space reasoning (imagination) and realizable world dynamics (reality). Trained on large-scale video-action pairs spanning diverse domains, PAN supports open-domain, action-conditioned simulation with coherent, long-term dynamics. Extensive experiments show that PAN achieves strong performance in action-conditioned world simulation, long-horizon forecasting, and simulative reasoning compared to other video generators and world models, taking a step towards general world models that enable predictive simulation of future world states for reasoning and acting.
PAN, a general, interactable, and long-horizon world model, predicts future world states using a Generative Latent Prediction (GLP) architecture that combines autoregressive latent dynamics with a video diffusion decoder, enabling detailed, long-term, and coherent video simulation.
world model video generation causal control interactivity long-horizon consistency prompt-to-full-video Generative Latent Prediction (GLP) autoregressive latent dynamics large language model (LLM) video diffusion decoder perceptually detailed temporally coherent open-domain action-conditioned simulation long-horizon forecasting simulative reasoning