标题： LLaDA-MedV：探索用于生物医学图像理解的大型语言扩散模型 显示英文标题

标题： LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding

摘要： 自回归模型（ARMs）长期以来在生物医学视觉语言模型（VLMs）领域占据主导地位。最近，如LLaDA之类的掩码扩散模型作为有前途的替代方案出现，但它们在生物医学领域的应用仍大多未被探索。为了填补这一空白，我们引入了\textbf{LLaDA-MedV}，这是第一个通过视觉指令微调专门用于生物医学图像理解的大语言扩散模型。LLaDA-MedV在开放式生物医学视觉对话任务中相对于LLaVA-Med提高了7.855%的性能，相对于LLaDA-V提高了1.867%，并在三个VQA基准测试的封闭形式子集上设定了新的最先进准确率：在VQA-RAD上达到84.93%，在SLAKE上达到92.31%，在PathVQA上达到95.15%。此外，与LLaVA-Med的详细比较表明，LLaDA-MedV能够通过显式控制响应长度生成相对更长的响应，这可能导致更丰富的输出。我们还对训练和推理阶段进行了深入分析，强调了初始化权重选择、微调策略以及采样步骤与响应重复之间的相互作用的关键作用。代码和模型权重已发布在https://github.com/LLM-VLM-GSL/LLaDA-MedV。 显示英文摘要

摘要： Autoregressive models (ARMs) have long dominated the landscape of biomedical vision-language models (VLMs). Recently, masked diffusion models such as LLaDA have emerged as promising alternatives, yet their application in the biomedical domain remains largely underexplored. To bridge this gap, we introduce \textbf{LLaDA-MedV}, the first large language diffusion model tailored for biomedical image understanding through vision instruction tuning. LLaDA-MedV achieves relative performance gains of 7.855\% over LLaVA-Med and 1.867\% over LLaDA-V in the open-ended biomedical visual conversation task, and sets new state-of-the-art accuracy on the closed-form subset of three VQA benchmarks: 84.93\% on VQA-RAD, 92.31\% on SLAKE, and 95.15\% on PathVQA. Furthermore, a detailed comparison with LLaVA-Med suggests that LLaDA-MedV is capable of generating reasonably longer responses by explicitly controlling response length, which can lead to more informative outputs. We also conduct an in-depth analysis of both the training and inference stages, highlighting the critical roles of initialization weight selection, fine-tuning strategies, and the interplay between sampling steps and response repetition. The code and model weight is released at https://github.com/LLM-VLM-GSL/LLaDA-MedV.