Streaming video understanding requires models to continuously retain useful visual evidence before future questions are known. Existing approaches primarily manage the growing visual context according to token importance, temporal redundancy, or segment-level relevance, but rarely organize evidence around objects that...
Mingkang Dong, Muxin Pu, Jie Li et al.· arXiv.org· 1 citation
A cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios, and two official Codabench tracks.
Yu-Qian Fu, Tianwen Qian, Yanjun Li et al.· 0 citations
WM-R1 is the first reinforcement learning framework that trains mobile GUI agents with world models instead of real environments, eliminating the need for real-environment interaction, supports massively parallelized and step-level granularized trajectory generation grounded in world models, and introduces a multi-dime...
Yu Han, Tianwen Qian· 0 citations
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