Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...
Jin-Zhou Tang, Z. Zhang, Jing Yang et al.· 0 citations
Embodied world models learn to predict future physical dynamics from visual observations and control signals, where physical knowledge is implicitly entangled within latent representations. We introduce CausalWM, a 16B embodied world model that performs explicit causal chain-of-thought reasoning before future video pre...
Zi-Ming Xu, Shuang Liang, R. Han et al.· 1 citation
Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...
Jin-Zhou Tang, Z. Zhang, Jing Yang et al.· 0 citations
A cognitive dual-process planning framework that represents planning-relevant scene knowledge in a machine-parsable structured chain-of-thought (S-CoT) schema and shows how explicit scene knowledge can be operationalized through adaptive reasoning and rule-based verification to support high-level VLM planning decisions...
Zhongyao Yang, Haoyu Li, Yuchen Yan et al.· arXiv.org· 0 citations
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