Author

Zhao-Chong An

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Preprint Oct 2026

Native Action-Prior Learning from Videos for World Action Models

World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observation-only videos contain rich evidence about interaction dynamics, but existing approaches typically use them either to pretrain visual rep...

Zhao-Chong An, Fei Zhang, Meng-Lin Jia et al. · 0 citations

#artificial intelligence Preprint Oct 2026

World Action Modeling with Progressive Visual Planning

World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video rollouts is highly inefficient. Some recent WA...

Fei Zhang, Zhao-Chong An, Duncan P. Frost et al. · 0 citations

#artificial intelligence Review Sep 2026

Generative Embodied Multiple Behavior Control Systems for Human-like Agents

The efficacy of GEMS indicates the benefits of leveraging habitual behavior and multiple behavior control system coordination for believable embodied human-like agents.

Chong-Yu Bao, Hao-Kai Yang, Yu-Han Wang et al. · 0 citations

Jul 2026

Seeing or Knowing? Visual Context Sensitivity in Multimodal Large Language Models

For the coarse attributes the authors study, MLLMs encode the visual evidence but cannot reliably control their reliance on it, indicating that for the coarse attributes they study, MLLMs cannot reliably control their reliance on it.

Jiaang Li, Chengzu Li, Zhaochong An et al. · arXiv.org · 0 citations