Recent advances in visuospatial understanding and agentic reasoning enable direct robotic manipulation with general-purpose multimodal models, but their embodied capabilities remain poorly understood. This paper systematically evaluate GPT-6 Astra for closed-loop robotic manipulation using a lightweight agent execution...
You-Wei Zhou, Bo-Han Hou, Yan-Kai Fu et al.· 0 citations
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
OWP is proposed, a training-free framework that shifts hallucination mitigation from output-level manipulation to conflict-conditioned representation rebalancing and adaptively rebalances fused representations by suppressing misleading priors and restoring ignored evidence before decoding.
Zi-Jian Liu, Chaoyou Fu, Yan Yang 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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