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Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a signif...
TextVBench is introduced, a benchmark and evaluation framework that formulates visual-text evaluation as a multi-target, multi-instance spatiotemporal correspondence problem, and is organized around controlled text, layout, carrier, and motion attributes so that aggregate scores can be traced back to specific capabilit...
MoDeVLA is proposed, the first rate-distortion driven efficient VLA model that performs token-wise depth allocation via Mixture-of-Depth Conditioning and integrates shallow visual-spatial with deep textual-logical features for action conditioning and introduces Effective-Edge Flow, an action-aligned attribution measure...
Vision-Language-Action (VLA) model plays a crucial role in embodied decision making. While practical deployment requires fast inference under limited onboard computation, a full forward pass through the vision-language model makes such deployment challenging. To address this issue, existing methods typically employ lig...
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