Author

Chang Nie

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Preprint Oct 2026

Video-MemComp: Taylor-Guided Sensitivity-Aware Memory Management for Streaming Video Understanding

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

Preprint Jul 2026

Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory

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