In-context learning (ICL) enables Large Language Models (LLMs) to learn tasks from demonstration examples without parameter updates. Although it has been extensively studied in LLMs, its effectiveness in Vision-Language Models (VLMs) remains underexplored. In this work, we present a systematic study of ICL in VLMs, evaluating seven models spanning four architectures on three image captioning benchmarks. We analyze how prompt design, architectural choices, and training strategies influence multimodal ICL. To our knowledge, we are the first to analyze how attention patterns in VLMs vary with an increasing number of in-context demonstrations. Our results reveal that training on imag-text interleaved data enhances ICL performance but does not imply effective integration of visual and textual information from demonstration examples. In contrast, instruction tuning improves instruction-following but can reduce reliance on in-context demonstrations, suggesting a trade-off between instruction alignment and in-context adaptation. Attention analyses further show that current VLMs primarily focus on textual cues and fail to leverage visual information, suggesting a limited capacity for multimodal integration. These findings highlight key limitations in the ICL abilities of current VLMs and provide insights for enhancing their ability to learn from multimodal in-context examples.
Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhejiang University（浙江大学）；Beijing University of Technology（北京工业大学）
Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；School of Computer Science, Chongqing University, China（重庆大学计算机学院）
PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学庆元研究院）；Shanghai Innovation Institute（上海创新研究院）；Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院病理科）；Intelligent Pathology Institute, Division of Life Sciences and Medicine（生命科学与医学学院智能病理研究所）；Department of Pathology, Fudan University Shanghai Cancer Center（复旦大学上海癌症中心病理科）；Department of Oncology, Shanghai Medical College, Fudan University（复旦大学上海医学院肿瘤科）；Institute of Pathology, Fudan University（复旦大学病理研究所）；Department of Pathology, The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院病理科）
See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm
University College London（伦敦大学学院）；Imperial College London（伦敦帝国学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；ShanghaiTech University（上海科技大学）；Southern University of Science（南方科技大学）
Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
Renmin University of China（中国人民大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chinese University of Hong Kong（香港中文大学）；Johns Hopkins University（约翰霍普金斯大学）；Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）
Language Models Can See Better: Visual Contrastive Decoding For LLM Multimodal Reasoning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
School of Computing；Augmented Intelligence, Arizona State University, Tempe, AZ, USA；Department of Computer Science；Engineering, Texas A\&M University, College Station, TX, USA
Multimodal Language Models See Better When They Look Shallower
Zhejiang Gongshang University（浙江工商大学）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）；Institute of Digital Twin, Eastern Institute of Technology, Ningbo（数字孪生研究院，东部技术研究所，宁波）；Meituan Inc.（美团公司）；National University of Singapore（新加坡国立大学）
Shanghai Jiao Tong University（上海交通大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centra…
Investigating the effectiveness of multimodal data in forecasting SARS-COV-2 case surges
Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局生物信息研究所）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Amsterdam University Medical Center, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹大学医学中心）
Investigating Faithfulness in Large Audio Language Models
Concordia University（康科迪亚大学）；Mila - Quebec AI Institute（魁北克人工智能研究院）；Université Laval（拉瓦尔大学）；Birla Institute of Technology and Science, Pilani（比拉理工学院和科学学院，皮兰尼）
Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人与智能系统国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
