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Yi-Kun Miao

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Preprint Sep 2026

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, proprioceptive, and action tokens. Quantization reduces inference cost, but comparable numerical errors in different streams can have markedly...

Yun-Han Wang, Hao-Dong Wang, Zhi-Ming Liu et al. · 0 citations

#machine learning Preprint Sep 2026

OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling

Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling. While recent efforts leverage simulator-generated data to enhance this capability, existing training pipelines face two fundamental limitations. First, static offline...

Yi-Kun Miao, Fang-Qi Zhu, Quan-Xin Shou et al. · 2 citations

#robotics Preprint Jul 2026

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy

Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused by distribution shifts during deployment. While offline reinforcement learning (RL) provides a practical way to improve deployed policies using rollout data, existing...

Zhengyang Yan, Junhao Li, Fangqi Zhu et al. · 2 citations