NVIDIA（英伟达）

Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions

比较推理：让音频语言模型更擅长比较情感

Abinay Reddy Naini, Jaeyeon Kim, Chao-Han Huck Yang, Shinji Watanabe, Carlos Busso

机构 * Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所） ； The University of Texas at Dallas（德克萨斯大学达拉斯分校） ； NVIDIA（英伟达） 纠错

AI总结 提出一种推理引导的序数情感识别框架，通过配对语音输入和推理轨迹训练音频语言模型，实现可解释的比较情感判断，仅需传统方法5%的训练数据。

SHERLOC: Structured Diagnostic Localization for Code Repair Agents

SHERLOC: 代码修复智能体的结构化诊断定位

Hovhannes Tamoyan, Sean Narenthiran, Erik Arakelyan, Mira Mezini, Boris Ginsburg

机构 * NVIDIA（英伟达） ； Santa Clara, CA 95051, USA（美国加利福尼亚州圣克拉拉） ； TU Darmstadt（达姆施塔特工业大学） ； National Research Center for Applied Cybersecurity ATHENE（国家应用网络安全研究中心 ATHENE） 纠错

AI总结 提出SHERLOC框架，通过推理LLM与紧凑仓库工具及自恢复机制，实现无需微调的结构化诊断定位，在SWE-Bench上达到最优定位精度，并提升修复率、降低令牌消耗。

Social Structure Matters in 3D Human-Human Interaction Generation

社会结构在三维人-人交互生成中的重要性

Zhongju Wang, Beier Wang, Yatao Bian, Pichao Wang, Zhi Wang, Daoyi Dong, Hongdong Li, Huadong Mo, Zhenhong Sun

机构 * University of New South Wales（新南威尔士大学） ； National University of Singapore（新加坡国立大学） ； NVIDIA（英伟达） ； Nanjing University（南京大学） ； University of Technology Sydney（悉尼科技大学） ； Australian National University（澳大利亚国立大学） 纠错

AI总结 提出Solo-to-Social框架，利用LLM规划社会结构（阶段分解、角色分配）并指导运动执行器生成协调的双人3D运动，解决文本驱动的人-人交互生成问题。

MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?

MapReason-OSM：视觉语言模型能否从街道地图中做出可图验证的移动决策？

Srinivas Venkatanarayanan, Clement Pakkam Isaac

机构 * NVIDIA（英伟达） ； University of Central Florida（中佛罗里达大学） ； University of South Florida（南佛罗里达大学） 纠错

AI总结 提出MapReason-OSM基准，通过自渲染OpenStreetMap面板和隐藏街道图，评估VLM在路由、设施定位等任务中做出可图验证的移动决策的能力，发现模型在图形成本推理和跨缩放一致性上表现不佳。

Comments 9 pages, 7 figures. Submitted to ACM SIGSPATIAL 2026 (Industrial Track). Code and data: https://github.com/Vi-Sri/mapreason-osm

Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation

数据强制蒸馏：恢复少步视频生成中的多样性和保真度

Siyi Chen, Shaowei Liu, Yixuan Jia, Zian Wang, Huan Ling, Qing Qu, Jun Gao

机构 * University of Michigan（密歇根大学） ； NVIDIA（英伟达） ； University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

AI总结 针对分布匹配蒸馏（DMD）在少步视频生成中出现的模式坍塌和过饱和问题，提出数据强制蒸馏（DFD）框架，通过教师评分差异引导学生接近真实数据分布，仅需一行代码修改即可恢复多样性和保真度。

Cosmos 3: Omnimodal World Models for Physical AI

Cosmos 3：面向物理AI的全模态世界模型

NVIDIA, :, Aditi, Niket Agarwal, Arslan Ali, Jon Allen, Martin Antolini, Adeline Aubame, Alisson Azzolini, Junjie Bai, Maciej Bala, Yogesh Balaji, Josh Bapst, Aarti Basant, Mukesh Beladiya, Mohammad Qazim Bhat, Zaid Pervaiz Bhat, Dan Blick, Vanni Brighella, Han Cai, Tiffany Cai, Eric Cameracci, Jiaxin Cao, Yulong Cao, Mark Carlson, Carlos Casanova, Ting-Yun Chang, Yan Chang, Yu-Wei Chao, Prithvijit Chattopadhyay, Roshan Chaudhari, Chieh-Yun Chen, Junyu Chen, Ke Chen, Qizhi Chen, Wenkai Chen, Xiaotong Chen, Yu Chen, An-Chieh Cheng, Click Cheng, Xiu Chia, Jeana Choi, Chaeyeon Chung, Wenyan Cong, Yin Cui, Magdalena Dadela, Nalin Dadhich, Wenliang Dai, Joyjit Daw, Alperen Degirmenci, Rodrigo Vieira Del Monte, Robert Denomme, Sameer Dharur, Marco Di Lucca, Ke Ding, Wenhao Ding, Yifan Ding, Yuzhu Dong, Nicole Drumheller, Yilun Du, Aigul Dzhumamuratova, Aleksandr Efitorov, Hamid Eghbalzadeh, Naomi Eigbe, Imad El Hanafi, Hassan Eslami, Benedikt Falk, Jiaojiao Fan, Jim Fan, Amol Fasale, Sergiy Fefilatyev, Liang Feng, Francesco Ferroni, Sanja Fidler, Xiao Fu, Vikram Fugro, Prashant Gaikwad, TJ Galda, Katelyn Gao, Yihuai Gao, Wenhang Ge, Sreyan Ghosh, Arushi Goel, Vivek Goel, Akash Gokul, Rama Govindaraju, Jinwei Gu, Miguel Guerrero, Elfie Guo, Aryaman Gupta, Siddharth Gururani, Hugo Hadfield, Song Han, Ankur Handa, Zekun Hao, Mohammad Harrim, Ali Hassani, Nathan Hayes-Roth, Yufan He, Chris Helvig, Cyrus Hogg, Madison Huang, Michael Huang, Sophia Huang, Yufan Huang, Jacob Huffman, DeLesley Hutchins, Suneel Indupuru, Boris Ivanovic, Arihant Jain, Joel Jang, Ryan Ji, Yanan Jian, Dongfu Jiang, Jingyi Jin, Atharva Joshi, Nikhilesh Joshi, Pranjali Joshi, Andy Ju, Jaehun Jung, Weiwei Kang, Scott Kassekert, Jan Kautz, Ashna Khetan, Julia Kiczka, Slawek Kierat, Gwanghyun Kim, Kuno Kim, Sunny Kim, Kezhi Kong, Xin Kong, Zhifeng Kong, Tomasz Kornuta, Egor Krivov, Hui Kuang, Saurav Kumar, Chia-Wen Kuo, George Kurian, Wojciech Kutak, JF Lafleche, Himangshu Lahkar, Omar Laymoun, Jayjun Lee, Sanggil Lee, Gabriele Leone, Boyi Li, Freya Li, Jiajun Li, Jinfeng Li, Ling Li, Pengcheng Li, Shangru Li, Tingle Li, Xiaolong Li, Xuan Li, Zhaoshuo Li, Zhiqi Li, Hao Liang, Maosheng Liao, Chen-Hsuan Lin, Tsung-Yi Lin, Ming-Yu Liu, Sifei Liu, Zihan Liu, Hai Loc Lu, Xiangyu Lu, Alice Luo, Ruipu Luo, Wenjie Luo, Jiangran Lyu, Martin Ding Ma, Nic Ma, Qianli Ma, Dawid Majchrowski, Louis Marcoux, Miguel Martin, Qing Miao, Ashkan Mirzaei, Shreyas Misra, Kaichun Mo, Durra Mohsin, Hyejin Moon, Pawel Morkisz, Saeid Motiian, Kirill Motkov, Seungjun Nah, Yashraj Narang, Deepak Narayanan, Thabang Ngazimbi, Julian Ouyang, Shubham Pachori, David Page, Yatian Pang, Sehwi Park, Mahesh Patekar, Mostofa Patwary, Marco Pavone, Trung Pham, Wei Ping, Soha Pouya, Shrimai Prabhumoye, Varun Praveen, Delin Qu, Hesam Rabeti, Morteza Ramezanali, Marilyn Reeb, Xuanchi Ren, Kristen Rumley, Wojciech Rymer, Jun Saito, Yeongho Seol, John Shao, Piyush Shekdar, Tianwei Shen, Humphrey Shi, Min Shi, Stella Shi, Kevin Shih, Mohammad Shoeybi, Mateusz Sieniawski, Shuran Song, Alexander Sotelo, Amir Sotoodeh, Sunil Srinivasa, Vignesh Srinivasakumar, Bartosz Stefaniak, Rahul Heinrich Steiger, Shangkun Sun, Jiaxiang Tang, Shitao Tang, Yangyang Tang, Yue Tang, Tolou Tavakkoli, Kayley Ting, Krzysztof Tomala, Wei-Cheng Tseng, Jibin Varghese, Sergei Vasilev, Thomas Volk, Raju Wagwani, Roger Waleffe, Andrew Z. Wang, Boxiang Wang, Haoxiang Wang, Qiao Wang, Shihao Wang, Shijie Wang, Ting-Chun Wang, Yan Wang, Yu Wang, Rohit Watve, David Wehr, Fangyin Wei, Xinshuo Weng, Jay Zhangjie Wu, Kedi Wu, Hongchi Xia, Summer Xiao, Tianjun Xiao, Kevin Xie, Daguang Xu, Jiashu Xu, Mengyao Xu, Ruqing Xu, Xingqian Xu, Yao Xu, Dinghao Yang, Dong Yang, Hans Yang, Xiaodong Yang, Xuning Yang, Yichu Yang, Yurong You, Zhiding Yu, Hao Yuan, Simon Yuen, Xiaohui Zeng, Pengcuo Zeren, Cindy Zha, Haotian Zhang, Jenny Zhang, Jing Zhang, Liangkai Zhang, Paris Zhang, Shun Zhang, Xuanmeng Zhang, Zhizheng Zhang, Ann Zhao, Yilin Zhao, Yuliya Zhautouskaya, Charles Zhou, Fengzhe Zhou, Shilin Zhu, Yuke Zhu, Dima Zhylko, Artur Zolkowski

机构 * NVIDIA 纠错

AI总结 提出基于统一混合Transformer架构的全模态世界模型Cosmos 3，联合处理语言、图像、视频、音频和动作序列，在理解和生成任务上达到新最优，为具身智能体提供可扩展的通用骨干。

Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study

基于人工智能从非对比MR成像预测脑肿瘤强化：一项多队列回顾性诊断准确性研究

James K Ruffle, Samia Mohinta, Guilherme Pombo, Asthik Biswas, Alan Campbell, Indran Davagnanam, David Doig, Ahmed Hammam, Harpreet Hyare, Farrah Jabeen, Emma Lim, Dermot Mallon, Stephanie Owen, Sophie Wilkinson, Sebastian Brandner, Parashkev Nachev

机构 * Queen Square Institute of Neurology, University College London, London, UK（伦敦大学学院医院神经科学研究所） ； National Hospital for Neurology and Neurosurgery, London, UK（伦敦神经病学与神经外科医院） ； NVIDIA, UK（英国NVIDIA公司） ； Great Ormond Street Hospital for Children, London, UK（伦敦儿童医院） ； Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK（斯坦莫尔皇家骨科医院，中西敏，英国） ； University College Hospitals NHS Foundation Trust, London, UK（伦敦大学学院医院 NHS 基础信托） ； Royal Free Hospital, London, UK（伦敦皇家自由医院） ； Imperial College Healthcare NHS Trust, London, UK（伦敦帝国学院医疗信托） ； Imperial College London, London, UK（伦敦帝国学院） 纠错

AI总结 本研究开发并验证了深度学习模型，仅从非对比MRI预测肿瘤对比增强，在多个数据集上达到83.0%的平衡准确率，有望减少神经肿瘤成像中对钆的依赖。

Comments 44 pages