Multimodal large language models (MLLMs) can now process hour-long videos, making extended multi-turn dialogue over a single video a natural mode of use. Sustaining such a dialogue requires more than answering each question in isolation: the model must retrieve and reuse information established earlier in the conversat...
Zheng-Dong Hu, Chao Wang, Kai-Tuo Feng et al.· 0 citations
Machine-learning models for metal-organic frameworks (MOFs) predominantly rely on atomic structures, whereas electron-density (ED) and electrostatic-potential (ESP) fields remain underexplored as scalable learning representations. These continuous fields encode complementary spatial and electronic information relevant...
Zhao-Lin Hu, He-He Fan, Chen-Hao Rao et al.· 0 citations
Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE reconstruction degradation. The 8x spatial compression in the VAE encoder-decoder degrades surface normals at object boundaries; even encoding and decoding ground-truth no...
SmartMage is proposed, a unified MLLM that dynamically orchestrates heterogeneous modalities for semantic-aware 3D scene understanding and achieves state-of-the-art performance across five 3D scene understanding benchmarks, and attains competitive results on RGB-only video understanding benchmarks.
SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise and a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement.
Yue Zhang, Zhi-Liang Wu, Yuxuan Hou et al.· Proceedings of the Thirty-Fi...· 0 citations
LEMO Agent is presented, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space that enriches high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity.
Zhaolin Hu, Hehe Fan, Wangyihan Guo et al.· 0 citations
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