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
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead. Existing workflows typically treat RL and distillation as disconnected stages: applying RL before distillation incurs prohibitive computational costs, whereas applyin...
Jiu-Zhou Lin, Jun-Long Wu, Feilong Zuo et al.· 0 citations
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, Shijie Ma, Yunxiao Wang et al.· 0 citations
Transformers lack a native lookup mechanism, requiring repeated dense computation to recognize and reuse local static patterns. Lngram v1 introduces tokenizer-independent conditional memory through discrete latent n-gram addressing, but its memory capacity is coupled with the backbone width, limiting scalability due to...
Yun Zheng, Bin Wen, Xiao-Jie Wang et al.· 0 citations
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