点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Yuqing Wen等 编辑丨具身智能之心 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 近年来，视觉-语言模型（Vision-Language Models, VLMs）取得了飞跃式进展。其中，自回归模型长期占据主导地位，展现了强大的多模理解与泛化能力，并推 动视觉-语言-动作模型（Vision-Language-Action Models, VLAs）成为了机器人智能控制的研究热点。然而，自回归模型的单向顺序生成机制在效率与灵活性上存 在天然瓶瓶颈。为突破这一困境， 掩码扩散模型 （Masked Diffusion Models, MDMs）强势崛起，凭借并行预测与多轮迭代优化，在大规模预训练下展现出于自 回归模型可比的性能与可扩展性，代表性的工作有 大语言扩散模型 LLaDA，以及其多模态拓展LLaDA-V等。 然而，大语言扩散模型在 机器人动作生成 上的价值仍未被充分挖掘。为此，我们提出 LLaDA-VLA —首个大语言扩散模型开发的 ...

Core Insights - The article discusses the recent ICML 2025 conference, highlighting the award-winning papers and the growing interest in AI research, evidenced by the increase in submissions and acceptance rates [3][5]. Group 1: Award-Winning Papers - A total of 8 papers were awarded this year, including 6 outstanding papers and 2 outstanding position papers [3]. - The conference received 12,107 valid paper submissions, with 3,260 accepted, resulting in an acceptance rate of 26.9%, a significant increase from 9,653 submissions in 2024 [5]. Group 2: Outstanding Papers - **Paper 1**: Explores masked diffusion models (MDMs) and their performance improvements through adaptive token decoding strategies, achieving a solution accuracy increase from less than 7% to approximately 90% in logic puzzles [10]. - **Paper 2**: Investigates the role of predictive technologies in identifying vulnerable populations for government assistance, providing a framework for policymakers [14]. - **Paper 3**: Introduces CollabLLM, a framework enhancing collaboration between humans and large language models, improving task performance by 18.5% and user satisfaction by 17.6% [19]. - **Paper 4**: Discusses the limitations of next-token prediction in creative tasks and proposes new methods for enhancing creativity in language models [22][23]. - **Paper 5**: Reassesses conformal prediction from a Bayesian perspective, offering a practical alternative for uncertainty quantification in high-risk scenarios [27]. - **Paper 6**: Addresses score matching techniques for incomplete data, providing methods that perform well in both low-dimensional and high-dimensional settings [31]. Group 3: Outstanding Position Papers - **Position Paper 1**: Proposes a dual feedback mechanism for peer review in AI conferences to enhance accountability and quality [39]. - **Position Paper 2**: Emphasizes the need for AI safety to consider the future of work, advocating for a human-centered approach to AI governance [44].