Vision-Language Models (VLMs) have advanced rapidly in static visual understanding, yet remain unreliable when judging how an egocentric task is progressing. Given a task instruction and two visual observations, a model should determine which state is closer to the goal by analyzing task-relevant object configurations...
Xiao-Da Yang, Can Wang, Yu-Xiang Liu et al.· 0 citations
Video world models must preserve the visual state of the world over time, but existing evaluation protocols often rely on generated histories, video reference, or selected revisit viewpoints that can confound the assessment of a model's true memory capability. To address this, we introduce OPIS, an input-grounded bench...
Hao Wang, Tao Yu, Liu-Zhou Zhang et al.· 0 citations
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whos...
AgiBot Research Team, Renhang Liu, Wen-Zhi Zhao et al.· 2 citations· ⚡1
Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.
Xiaowei Cai, Yunuo Cai, Bing Chen et al.· 1 citation
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