Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains understudied. We investigate $\boldsymbol{\mathsf{transferability}}$ in UMMs: whether training a capability on one task improves the same capability on the other without explicit supervision. Through controlled experiments, we empirically find that transferability depends on architecture-models with fully shared transformer backbone and a unified visual encoder exhibit consistent cross-task transfer, while loosely coupled designs show little or none. Leveraging this transferability, we propose a practical training strategy. The most straightforward way to improve a target generative capability (e.g., counting) is to fine-tune generation directly, but this can degrade visual quality due to distribution shift. Instead, we train the corresponding understanding task and let it transfer into generation, which improves capability-specific generative performance while minimizing distribution shift. We validate this across three capabilities-counting, spatial relation, and text recognition/generation-showing that cross-task transferability can be systematically exploited in UMMs.
Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of Oxford（牛津大学）；Drexel University（德雷塞尔大学）；Beijing University of Technology（北京理工大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Xi’an Jiaotong University（西安交通大学）
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Academy for Engineering & Technology, Fudan University（复旦大学工程技术学院）；Shanghai Key Lab of Intelligent Information Processing（上海智能信息处理重点实验室）；Shanghai Engineering Research Center of AI & Robotics（上海人工智能与机器人工程研究中心）；Engineering Research Center of AI & Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Monash University（莫纳什大学）；Temple University（天普大学）；Peng Cheng Laboratory（鹏城实验室）
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation
School of Mathematics, Sun Yat-Sen University（中山大学数学学院）；School of Statistics and Mathematics, Guangdong University of Finance and Economics（广东财经大学统计与数学学院）；School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机学院）
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；University of Science and Technology of China（中国科学技术大学）；Hangzhou Dianzi University（杭州电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）
HaploOmni: Unified Single Transformer for Multimodal Video Understanding and Generation
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；The University of Hong Kong（香港大学）；Xi’an JiaoTong University（西安交通大学）
ARMOR: Empowering Multimodal Understanding Model with Interleaved Multimodal Generation Capability
Nankai University（南开大学）；Shanghai Innovation Institute（上海创新研究院）；University of Science and Technology of China（中国科学技术大学）；Wuhan University（武汉大学）；Shanghai AI Laboratory（上海人工智能实验室）
Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability
School of Cyber Science and Engineering, Huazhong University of Science and Technology（电子科学与工程学院，华中科技大学）；School of Software Engineering, Huazhong University of Science and Technology（软件工程学院，华中科技大学）；School of Information and Communication Technology, Griffith University（信息与通信技术学院，格里菲斯大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）
Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；Tencent（腾讯）；Center for Data Science, Peking University（北京大学数据科学中心）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）
EHWGesture -- A dataset for multimodal understanding of clinical gestures
Department of Control and Computer Engineering, Politecnico di Torino（控制与计算机工程系，都灵理工大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；CNR-IEIIT（意大利国家研究委员会-IEIIT）
Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；Purple Mountain Laboratories（紫金山实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
Zhejiang University（浙江大学）；The University of Sydney（悉尼大学）；SenseTime Research（商汤科技研究院）；Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）
SurgLLM: A Versatile Large Multimodal Model with Spatial Focus and Temporal Awareness for Surgical Video Understanding
Hong Kong Institute of Science & Innovation（香港科学与工业创新研究院）；CAMP, Technische Universität München（CAMP，慕尼黑技术大学）；Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong（香港中文大学医学院外科部）
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
LIACS, Leiden University（莱顿大学高级计算机科学研究所）；College of Systems Engineering, NUDT（国防科技大学系统工程学院）；Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）
School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院，东南大学，南京，中国）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其跨学科应用关键实验室（东南大学），教育部，中国）
针对多模态视频模型中强模态主导导致的过拟合与模态不平衡问题，提出 MM 及改进的 B-MM 方法，通过动态调整模态混合比例提升泛化能力与鲁棒性。
LVRPO: Language-Visual Alignment with GRPO for Multimodal Understanding and Generation
Department of Machine Learning, CMU, USA（卡内基梅隆大学机器学习系，美国）；Department of Machine Learning, MBZUAI, UAE（穆罕默德·本·扎耶德人工智能大学机器学习系，阿联酋）；Department of Artificial Intelligence, Hanyang University ERICA, South Korea（汉阳大学ERICA校区人工智能系，韩国）
Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Multimodal Data Intelligent Perception and Governance（北京多模态数据智能感知与治理重点实验室）
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding
China Agricultural University（中国农业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；AniEye Technology（安尼眼科技）；State Key Laboratory of Animal Nutrition and Feeding（动物营养与饲料国家重点实验室）
