Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
GADE Union (Global AI Data Experts Union)（GADE联盟（全球人工智能数据专家联盟））；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；New York University（纽约大学）；Cambridge University（剑桥大学）；The University of Hong Kong（香港大学）
Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy overlooked by existing benchmarks that evaluate these abilities in isolation. To fill this void, we introduce Pix2Fact, a visual question-answering benchmark designed to assess expert-level visual perception and knowledge search. Pix2Fact comprises 1,000 high-resolution (4K+) images spanning eight scenarios. Its questions and answers are meticulously crafted by PhD-holding annotators from top global universities across diverse disciplines. Each question requires detailed visual grounding and the integration of external knowledge. Evaluating ten state-of-the-art VLMs, including proprietary models such as Gemini-3.1-Pro and GPT-5.4, we find that Pix2Fact poses a formidable challenge: the most advanced model (Gemini-3.1-Pro) achieves only 51.7% average accuracy, even with access to visual ground truth and search tools. Our analysis attributes this low accuracy to three factors, frequent visual grounding errors even with visual ground truth, shallow search harnessing, and VLM's inability to retrieve long-tail, unstructured local information. This striking gap exposes the limitations of current models in assisting humans with real-world scenarios that demand overwhelming visual comprehension. We believe Pix2Fact will serve as a critical benchmark to drive the next generation of language-vision agents that seamlessly integrate fine-grained perception with robust knowledge search.
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs
Shenzhen University（深圳大学）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）；Sun Yat-sen University（中山大学）
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
Beijing Normal University, China（北京师范大学）；University of Exeter, United Kingdom（埃克塞特大学）；University College London, United Kingdom（伦敦大学学院）；Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates（穆罕默德·本·扎耶德人工智能大学）；University of Surrey, United Kingdom（萨里大学）
VinDr-CXR-VQA: A Visual Question Answering Dataset for Explainable Chest X-Ray Analysis with Multi-Task Learning
College of Engineering and Computer Science（工程与计算机科学学院）；VinUniversity（Vin大学）；VinUni-Illinois Smart Health Center（VinUni-伊利诺伊智能健康中心）；Vietnam National Cancer Hospital（越南国家癌症医院）；The Computer Vision and Medical AI Lab（计算机视觉与医学人工智能实验室）
3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks
ZJU-Angelalign R&D Center for Intelligence Healthcare（浙江大学智能医疗研发中心）；Zhejiang University（浙江大学）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）
SpineBench: Benchmarking Multimodal LLMs for Spinal Pathology Analysis
Beijing University of Posts and Telecommunications（北京邮电大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Peking Union Medical College Hospital（北京佑安医学大学医院）
Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）；Chinese University of Hong Kong（香港中文大学）
Benchmarking the Thinking Mode of Multimodal Large Language Models in Clinical Tasks
Bytedance（字节跳动）；Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；Stanford University（斯坦福大学）；University of Michigan（密歇根大学）
Latent Variable Models for Visual Question Answering
Imperial College London（帝国理工学院）；University of Sheffield（谢菲尔德大学）；Department of Computing, Imperial College London（帝国理工学院计算机系）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）
Fudan University（复旦大学）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德克萨斯农工大学）
