Author

Jifan Li

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Preprint Sep 2026

The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models

These findings motivate Action-Relevant Predictive States (ARPS), a compact predictive interface between the video and action experts that achieves 99.2% success on LIBERO and transfers to LIBERO-Plus without adaptation, reaching 87.3% and exceeding Fast-WAM by 39.2 percentage points.

Qiwen Gu, Jifan Li, Bing-Jie Gao et al. · 0 citations

Preprint Aug 2026

R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models

High similarity between first-visit and return frames does not necessarily show that a video world model remembered the scene; the intervening rollout may simply have changed very little. This ambiguity makes absolute revisit scores sensitive to rendering stability, repetitive content, and failed motion. We introduce \...

Qiwen Gu, Bingjie Gao, Rui Chen et al. · 0 citations

Preprint Aug 2026

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee...

Dream Team, Rui Chen, Xiangxiang Chu et al. · 5 citations