Long video understanding relies on video memory to overcome the context limits of multimodal large language models. Existing methods follow a build-then-reasoning pipeline: memory is built offline for the entire video, then reasoned over as a static source. In practice a long video is shared by several questions, and t...
Wei Chen, Xuan-Yu Zheng, Yan-Cheng Long et al.· 0 citations
The Global State Model (GSM), a causal encoder--decoder architecture that concentrates the selection and aggregation of long-range information in the encoding stage, is introduced, offering a shared-state architecture for efficient language modeling.
Yun Zheng, Bin Wen, Xiao-Jie Wang et al.· 0 citations
DM-Align is introduced, which derives a complementary gradient direction to guide the model toward human-preferred samples, and eliminates the need for multi-step reward evaluation and complex ODE-SDE conversions inherent in traditional RL.
Jiu-Zhou Lin, Jun-Long Wu, Feilong Zuo et al.· 1 citation
Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video and its parametric memory. While Thinking-with-Videos enables active temporal perception and Deep Research supports multi-step information seeking, the two capabilities are...
Wenqi Liu, Shi-Jie Ma, Yun-Xiao Wang et al.· 2 citations
Analysis shows that Lngram v2 substantially reduces both total and activated memory parameters while maintaining or improving language modeling performance, and shows that its discrete IDs preserve substantial semantic structure of continuous hidden states, enabling semantic recovery from IDs alone and stable ID--seman...
Yun Zheng, Bin Wen, Xiao-Jie Wang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
