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
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
MPAR-Bench is introduced, a bilingual English-Chinese benchmark that isolates reasoning breadth through multi-point associative reasoning, and case-level analysis shows that extended reasoning can overturn an initially correct hypothesis.
Si'an Xie, Jiaxu Liu, Biao Yang et al.· 0 citations
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