Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in graphs is inherently two-dimensional: graphs differ not only in what they describe (topic domains) but also in how they are represented (format domains). Most existing GFM benchmarks vary only topic domains, thereby obscuring how knowledge transfers across both dimensions. We present a new benchmark that jointly evaluates topic and format gaps across the full GFM pipeline, including multi-domain self-supervised pre-training and few-shot downstream adaptation, and provides a timely evaluation of recent GFMs in the rapidly evolving landscape. Our protocol enables controlled assessment in four settings: (i) pre-training on diverse topics and formats, while adapting to unseen downstream datasets; (ii) same pre-training as in (i), while adapting to seen datasets; (iii) pre-training on a single topic domain, while adapting to other topics; (iv) pre-training on a base format, while adapting to other formats. This two-axis evaluation disentangles semantic generalization from robustness to representational shifts. We conduct extensive evaluations of eight state-of-the-art GFMs on 33 datasets spanning seven topic domains and six format domains, surfacing new empirical observations and practical insights for future research. Codes/data are available at https://github.com/smufang/GFMBenchmark.
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Harvard Medical School（哈佛医学院）；Stanford University（斯坦福大学）；Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）
Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models
Vanderbilt University（范德比尔特大学）；Weill Cornell Medicine（韦尔医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Radiology（放射科）；Department of Computer Science（计算机科学系）；Department of Biostatistics（生物统计学系）；Department of Pathology, Microbiology and Immunology（病理学、微生物学和免疫学系）
Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；School of Earth and Space Exploration, Arizona State University（地球与空间探索学院，亚利桑那州立大学）；Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Professorship of Autonomous Vehicle Systems, Technical University of Munich, Munich, Germany（自动驾驶车辆系统教授职位，慕尼黑技术大学，德国慕尼黑）；Bayerische Motoren Werke AG, Munich, Germany（巴伐利亚发动机有限公司，德国慕尼黑）；Data Analytics and Machine Learning Group, Technical University of Munich, Munich, Germany（数据分析与机器学习小组，慕尼黑技术大学，德国慕尼黑）
SpecEval: Evaluating Model Adherence to Behavior Specifications
Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）；The Bradley Department of Electrical and Computer Engineering, Virginia Tech（电气与计算机工程系，弗吉尼亚理工学院）
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
University of California, Irvine（加州大学欧文分校）；Stanford University（斯坦福大学）；University of Pittsburgh（匹兹堡大学）；University of Toronto（多伦多大学）；HealthUnity；National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）
