LaViDa 模型通过更快、更可控的扩散技术推进多模态人工智能 · 跟踪 3 个来源

报道来源 [3]

1. arXiv cs.CV TIER_1 English(EN) · Shufan Li, Konstantinos Kallidromitis, Hritik Bansal, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Jason Kuen, Zhe Lin, Kai-Wei Chang, Aditya Grover · 2026-07-17 04:00

   LaViDa：用于多模态理解的大型扩散语言模型

   arXiv:2505.16839v4 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and controllable generation (e.g., constraining outpu…

2. arXiv cs.CV TIER_1 English(EN) · Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen · 2026-07-17 04:00

   Lavida-O: 用于统一多模态理解与生成的弹性大型掩码扩散模型

   arXiv:2509.19244v3 Announce Type: replace Abstract: We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-…

3. arXiv cs.CV TIER_1 Italiano(IT) · Shufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin, Zijun Wei, Aditya Grover, Jason Kuen · 2026-07-17 04:00

   Sparse-LaViDa: 稀疏多模态离散扩散语言模型

   arXiv:2512.14008v2 Announce Type: replace Abstract: Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to t…