WoW: Towards a World omniscient World model Through Embodied Interaction
Humans develop an understanding of intuitive physics through active
interaction with the world. This approach is in stark contrast to current video
models, such as Sora, which rely on passive observation and therefore struggle
with grasping physical causality. This observation leads to our central
hypothesis: authentic physical intuition of the world model must be grounded in
extensive, causally rich interactions with the real world. To test this
hypothesis, we present WoW, a 14-billion-parameter generative world model
trained on 2 million robot interaction trajectories. Our findings reveal that
the model's understanding of physics is a probabilistic distribution of
plausible outcomes, leading to stochastic instabilities and physical
hallucinations. Furthermore, we demonstrate that this emergent capability can
be actively constrained toward physical realism by SOPHIA, where
vision-language model agents evaluate the DiT-generated output and guide its
refinement by iteratively evolving the language instructions. In addition, a
co-trained Inverse Dynamics Model translates these refined plans into
executable robotic actions, thus closing the imagination-to-action loop. We
establish WoWBench, a new benchmark focused on physical consistency and causal
reasoning in video, where WoW achieves state-of-the-art performance in both
human and autonomous evaluation, demonstrating strong ability in physical
causality, collision dynamics, and object permanence. Our work provides
systematic evidence that large-scale, real-world interaction is a cornerstone
for developing physical intuition in AI. Models, data, and benchmarks will be
open-sourced.
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Peidong Jia
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Weishi Mi
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Kevin Zhang
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Wanxin Tian
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Wanxin Tian
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Kuangzhi Ge
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Hao Li
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Hao Li
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Zezhong Qian
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Anthony Chen
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Qiang Zhou
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Jiaming Liu
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Yong Dai
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Chengyu Bai
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Chengyu Bai
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Yu-Kai Wang
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Lizhang Chen
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Yong Bao
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Jiacheng Zhu
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Kai Tang
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Ruichuan An
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Yulin Luo
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Qiuxuan Feng
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Siyuan Zhou
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Chi-min Chan
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Chengkai Hou
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Wei Xue
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Sirui Han
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Sirui Han
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Yike Guo
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Shanghang Zhang
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Jian Tang
WoW: Towards a World omniscient World model Through Embodied Interaction
Humans develop an understanding of intuitive physics through active
interaction with the world. This approach is in stark contrast to current video
models, such as Sora, which rely on passive observation and therefore struggle
with grasping physical causality. This observation leads to our central
hypothesis: authentic physical intuition of the world model must be grounded in
extensive, causally rich interactions with the real world. To test this
hypothesis, we present WoW, a 14-billion-parameter generative world model
trained on 2 million robot interaction trajectories. Our findings reveal that
the model's understanding of physics is a probabilistic distribution of
plausible outcomes, leading to stochastic instabilities and physical
hallucinations. Furthermore, we demonstrate that this emergent capability can
be actively constrained toward physical realism by SOPHIA, where
vision-language model agents evaluate the DiT-generated output and guide its
refinement by iteratively evolving the language instructions. In addition, a
co-trained Inverse Dynamics Model translates these refined plans into
executable robotic actions, thus closing the imagination-to-action loop. We
establish WoWBench, a new benchmark focused on physical consistency and causal
reasoning in video, where WoW achieves state-of-the-art performance in both
human and autonomous evaluation, demonstrating strong ability in physical
causality, collision dynamics, and object permanence. Our work provides
systematic evidence that large-scale, real-world interaction is a cornerstone
for developing physical intuition in AI. Models, data, and benchmarks will be
open-sourced.
WoW, a 14-billion-parameter generative world model trained on robot interactions, demonstrates improved physical intuition through SOPHIA's guidance and achieves state-of-the-art performance on physical consistency and causal reasoning in video.
generative world model probabilistic distribution stochastic instabilities physical hallucinations vision-language model DiT-generated output Inverse Dynamics Model imagination-to-action loop WoWBench physical consistency causal reasoning physical causality collision dynamics object permanence