Evaluating GPT-5 as a Multimodal Clinical Reasoner: A Landscape Commentary
Department of Radiation Oncology, Winship Cancer Institute, Emory University School of Medicine（放射肿瘤科，Winship癌症研究所，埃默里大学医学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）
Alexandru Florea, Shansong Wang, Mingzhe Hu, Qiang Li, Zach Eidex, Luke del Balzo, Mojtaba Safari, Xiaofeng Yang
The transition from task-specific artificial intelligence toward general-purpose foundation models raises fundamental questions about their capacity to support the integrated reasoning required in clinical medicine, where diagnosis demands synthesis of ambiguous patient narratives, laboratory data, and multimodal imaging. This landscape commentary provides the first controlled, cross-sectional evaluation of the GPT-5 family (GPT-5, GPT-5 Mini, GPT-5 Nano) against its predecessor GPT-4o across a diverse spectrum of clinically grounded tasks, including medical education examinations, text-based reasoning benchmarks, and visual question-answering in neuroradiology, digital pathology, and mammography using a standardized zero-shot chain-of-thought protocol. GPT-5 demonstrated substantial gains in expert-level textual reasoning, with absolute improvements exceeding 25 percentage-points on MedXpertQA. When tasked with multimodal synthesis, GPT-5 effectively leveraged this enhanced reasoning capacity to ground uncertain clinical narratives in concrete imaging evidence, achieving state-of-the-art or competitive performance across most VQA benchmarks and outperforming GPT-4o by margins of 10-40% in mammography tasks requiring fine-grained lesion characterization. However, performance remained moderate in neuroradiology (44% macro-average accuracy) and lagged behind domain-specific models in mammography, where specialized systems exceed 80% accuracy compared to GPT-5's 52-64%. These findings indicate that while GPT-5 represents a meaningful advance toward integrated multimodal clinical reasoning, mirroring the clinician's cognitive process of biasing uncertain information with objective findings, generalist models are not yet substitutes for purpose-built systems in highly specialized, perception-critical tasks.
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation
Graduate School of AI, POSTECH（浦项科技大学人工智能研究生院）；Department of Computer Science and Engineering, POSTECH（浦项科技大学计算机科学与工程系）；Department of Chemical Engineering, POSTECH（浦项科技大学化学工程系）；KRAFTON
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary
Guangdong University of Technology（广东工业大学）；East China Jiaotong University（华东交通大学）；Emory University（埃默里大学）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；South China University of Technology（华南理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Amazon（亚马逊公司）；University of Toronto（多伦多大学）
When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL
University of Michigan Ann Arbor, MI, USA（密歇根大学安娜堡分校）；ByteDance Inc. San Jose, CA, USA（字节跳动公司）；University of Pennsylvania Philadelphia, PA, USA（宾夕法尼亚大学）
Large Language Models are Better Reasoners with Self-Verification
The Laboratory of Cognition and Decision Intelligence for Complex Systems, IA, CAS（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Unisound（云知声）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiaotong University（上海交通大学）；Nankai University（南开大学）；Shanghai University（上海大学）；Nanyang Technological University（南洋理工大学）；Hong Kong Polytechnic University（香港理工大学）；Tongji University（同济大学）；Nanjing University（南京大学）；University of California, Merced（加州大学梅德福分校）；Chinese University of Hong Kong（香港中文大学）
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
GeoDS Lab, Department of Geography, University of Wisconsin-Madison, USA（威斯康星大学麦迪逊分校地理系GeoDS实验室）；Department of Geography and Regional Research, University of Vienna, Austria（维也纳大学地理与区域研究系）；Department of Geography, University of California-Santa Barbara, USA（加州大学圣塔芭芭拉分校地理系）
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；Visual Intelligence + X International Joint Laboratory of the Ministry of Education（教育部视觉智能+X国际合作实验室）；Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所噪声与振动重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm
Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身智能研究所）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Key Laboratory of Multimodal Embodied AI（上海市多模态具身人工智能重点实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；The Chinese University of Hong Kong（香港中文大学）；Central South University（中南大学）；Fudan University（复旦大学）
