Vision language models (VLMs) are used in creative tasks such as visual storytelling, design ideation, art interpretation, and advertising. Yet we lack principled ways to measure the lateral reasoning of these models compared to human potential. We introduce CREATE, a semi-automatically constructed benchmark containing 155 visual Remote Associates Test problems. These problems require a solver to find an underlying connection among three seemingly dissimilar images. We conduct a human study (N=25) to validate and ground the quality of our benchmark, and evaluate 11 VLMs. Our findings show that most VLMs underperform humans on this task. Our experiments indicate that the associative ability of VLMs depends on the cue concepts provided in the text and does not universally extend to visual puzzles.
Probing and Inducing Combinational Creativity in Vision-Language Models
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Yuanpei College, Peking University（北京大学元培学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（人工智能通用基础理论国家重点实验室）；Center on Frontiers of Computing Studies, School of Computer Science, Peking University（计算机科学学院前沿计算研究中心）
Beijing University of Posts and Telecommunications（北京邮电大学）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab实验室）；The Allen Institute for AI（艾伦人工智能研究所）
The Safety Reminder: A Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算与信息系统学院）
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
1 Institute of Automation, Chinese Academy of Sciences；2 School of Artificial Intelligence, University of Chinese Academy of Sciences；3 Centre for Artificial Intelligence；Robotics, Hong Kong Institute of Science \& Innovation, CAS；4 Independent Researcher
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz AI（亥姆霍兹人工智能研究所）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）
On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AI
MICS, CentraleSupélec - Université Paris-Saclay, France（MICS，中央圣艾尔布兰大学-巴黎萨克雷大学，法国）；Google DeepMind, London, UK（谷歌DeepMind，伦敦，英国）；CONICET, Universidad de Buenos Aires, Argentina（CONICET，布宜诺斯艾利斯大学，阿根廷）
Vision-Language Models are Fragile Multilingual Associators
Manipal University Jaipur（斋浦尔马尼帕尔大学）；University of North Carolina Charlotte（北卡罗来纳大学夏洛特分校）；University of Salford（索尔福德大学）；University of Manchester（曼彻斯特大学）；Indian Statistical Institute Kolkata（印度统计研究所加尔各答分所）
Large Language Models Align with the Human Brain during Creative Thinking
EPFL（瑞士联邦理工学院洛桑）；Università della Svizzera italiana (USI)（意大利语区大学（瑞士））；Wesleyan University（卫斯理大学）；Paris Brain Institute (ICM)（巴黎大脑研究所）；Pennsylvania State University（宾夕法尼亚州立大学）
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
