Scaling humanoid motion foundation models is constrained by the scarcity of diverse, robot-executable data and by the difficulty of jointly learning from heterogeneous multimodal inputs while supporting real-time closed-loop control. We present Movens-5M, constructed from raw Internet videos through clip generation, qu...
Wen-Dong Bu, Ting-Hong Ye, Qi-Zhou Wang et al.· 0 citations
An event-centric evaluation framework that disentangles audio and visual events at the event level, analyzes each event independently, cross-validates relations across modalities, and aggregates the judgments into a coarse-to-fine, three-level consistency metric, providing a general formulation for audio-video consiste...
Yu-Ruo Wu, Kai-Hang Pan, Kai Shen et al.· 0 citations
Draft-Thinking is proposed, a lightweight adaptation framework that learns a compact, self-contained working derivation that directly supports the final answer in a long chain-of-thought reasoning model.
Jie Cao, Tianwei Lin, Zhen-Xuan Fan et al.· arXiv.org· 3 citations· ⚡1
Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal Large Language Models (MLLMs) offer enhanced visual perception and reasoning, their agentic deployment faces three challenges: costly data annotation, imprecise action-in...
Jia-He Ying, Wendong Bu, Kaihang Pan et al.· 0 citations
A large-scale robotic manipulation dataset and benchmark for diagnosing embodied reasoning in VLA models, and establishes RoboSPA as a challenging diagnostic benchmark for developing more capable, reliable, and generalizable embodied agents.
Zhen-Xuan Fan, Bo Zhang, Yu-Tong Lin et al.· 0 citations
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