RESEARCH PAPER

Being-H0.7: A Latent World-Action Model from Egocentric Videos

BeingBeyond Team (Hao Luo; Wanpeng Zhang; Yicheng Feng; Sipeng Zheng; Haiweng Xu; Chaoyi Xu; Ziheng Xi; Yuhui Fu; Zongqing Lu)

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Category review. A shared prior/posterior policy trains current-context latent queries to match future-informed representations, and those anticipatory queries guide action generation. This is an implicit latent world-action mechanism, with no explicit video rollout or IDM at inference. Reading evidence

AT A GLANCE

Contribution

Being-H0.7 trains a robot policy to anticipate useful future information inside latent queries. A future-aware training branch aligns its hidden states with a deployable branch that sees only current context, avoiding visual rollout during control. Strong benchmark and real-robot results support the complete system, while missing component ablations leave the causal contribution of future alignment unresolved (E02–E09, E12, E15).

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