World-action models use predicted visual futures to condition robot actions, yet execution feedback can invalidate parts of a prediction while leaving its task structure useful. We propose Revisable Temporal Planning (RTP), which maintains the visual future as a persistent action condition and revises it after feedback...
Peng Liu, Jun-Bo Niu, Wen-Hao Zheng et al.· 0 citations
Computer use agents (CUAs) have demonstrated strong capabilities in completing digital tasks. However, existing CUAs either rely solely on graphical user interface (GUI) interactions, which are often inefficient and error prone, or augment GUI interactions with application specific APIs or tools, which require substant...
Tong-I Chen, Jun-Bo Niu, Zhengxi Lu et al.· 0 citations
VES-Bench, a 600-question benchmark of Temporal Ordering and Event Counting items over 348 public long videos, is introduced and TRACE, a training-free agent that grounds answers in raw visual clips, builds an evidence bundle round by round, and stops only when the answer stabilises as the bundle grows and a final pass...
Peng Liu, Jun-Bo Niu, Xiaoyan Hu et al.· 2 citations
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