The rapid growth of video data has created an urgent need for efficient video understanding systems that can handle long and continuous visual inputs, posing significant challenges for Multimodal Large Language Models (MLLMs) due to severe memory bottlenecks. Moreover, existing compression methods often overlook the ex...
Yong-Hui Niu, Yu-Hao Ji, Chang Nie et al.· 0 citations
LLM-based agents face challenges in personalized interactive tasks due to heterogeneous, multi-typed, and implicitly constrained long-term traces. Existing memory mechanisms struggle with accurate routing and retrieval, especially on-device where personalization is critical. Most methods use single-vector representatio...
Ran Zhou, Xue Han, Jia-Heng Liu et al.· 0 citations
Light-Omni is introduced, a multimodal agent framework for reflexive and lightweight video understanding that achieves semantically aligned retrieval and reflexive responses while avoiding iterative reasoning and serves as a memory system to enhance both the performance and efficiency of existing MLLMs.
Chang Nie, Jiaju Wei, Jun-Lan Feng et al.· 0 citations
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