Human age estimation from facial images represents a challenging computer vision task with significant applications in biometrics, healthcare, and human-computer interaction. While traditional deep learning approaches require extensive labeled datasets and domain-specific training, recent advances in large vision-language models (LVLMs) offer the potential for zero-shot age estimation. This study presents a comprehensive zero-shot evaluation of state-of-the-art Large Vision-Language Models (LVLMs) for facial age estimation, a task traditionally dominated by domain-specific convolutional networks and supervised learning. We assess the performance of GPT-4o, Claude 3.5 Sonnet, and LLaMA 3.2 Vision on two benchmark datasets, UTKFace and FG-NET, without any fine-tuning or task-specific adaptation. Using eight evaluation metrics, including MAE, MSE, RMSE, MAPE, MBE, $R^2$, CCC, and $\pm$5-year accuracy, we demonstrate that general-purpose LVLMs can deliver competitive performance in zero-shot settings. Our findings highlight the emergent capabilities of LVLMs for accurate biometric age estimation and position these models as promising tools for real-world applications. Additionally, we highlight performance disparities linked to image quality and demographic subgroups, underscoring the need for fairness-aware multimodal inference. This work introduces a reproducible benchmark and positions LVLMs as promising tools for real-world applications in forensic science, healthcare monitoring, and human-computer interaction. The benchmark focuses on strict zero-shot inference without fine-tuning and highlights remaining challenges related to prompt sensitivity, interpretability, computational cost, and demographic fairness.
Benchmarking Open-Source Large Language Models for Persian in Zero-Shot and Few-Shot Learning
Department of Medical Engineering（医学工程系）；Electrical Engineering Department（电气工程系）；Sharif University of Technology（谢里夫技术大学）；Department of Electrical Engineering（电气工程系）；University of Tehran（德黑兰大学）
OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence
School of Software Technology, Zhejiang University（浙江大学软件学院）；Laboratory for Statistical Monitoring and Intelligent Governance of Common Prosperity, Zhejiang Gongshang University（浙江工商大学共同富裕统计监测与智能治理实验室）；Tongyi Lab, Alibaba Group（阿里集团通义实验室）；Alibaba Cloud（阿里云）
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis
ASUS Intelligent Cloud Services (AICS)（华硕智能云服务（AICS））；ASUS Global Pte. Ltd.（华硕全球私人有限公司）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心（CFAR））；Institute of High Performance Computing (IHPC)（高性能计算研究所（IHPC））；Agency for Science, Technology and Research (A*STAR)（新加坡科学、技术与研究局（A*STAR））
AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions
TMLR Group, Hong Kong Baptist University（香港 Baptist 大学 TMLR 团体）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sydney AI Centre, The University of Sydney（悉尼大学 AI 中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
LabOSBench: Benchmarking Computer Use Agents for Scientific Instrument Control
Shenzhen Loop Area Institute（深圳河套学院）；Dalian University of Technology（大连理工大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）
From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents
National University of Singapore（新加坡国立大学）；University of Toronto（多伦多大学）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；University of Oxford（牛津大学）
PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments
Department of Mathematics and Computer Science, Eindhoven University of Technology（埃因霍温理工大学数学与计算机科学系）；Department of Informatics, King’s College London（伦敦国王学院信息学系）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development
Shanghai Jiao Tong University, Shanghai, China（上海交通大学）；Beijing Institute of Technology, Beijing, China（北京理工大学）；University of California, Merced, CA, USA（加州大学默塞德分校）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院）
CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
CISPA - Helmholtz Center for Information Security（CISPA-海德堡信息安全研究中心）；NYU Tandon School of Engineering（纽约大学坦顿工程学院）；NYU Abu Dhabi（纽约大学阿布扎克分校）；IIIT Hyderabad（海得拉巴印度理工学院）
Benchmarking large language models for biomedical natural language processing applications and recommendations
Yale University（耶鲁大学）；National Institutes of Health（美国国立卫生研究院）；University of Texas Health Science at Houston（休斯顿德克萨斯大学健康科学中心）；University of Minnesota（明尼苏达大学）
VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs
Department of Computer Science, University of Sheffield, United Kingdom（英国谢菲尔德大学计算机科学系）；Signal Analysis and Interpretation Laboratory (SAIL), Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA（美国南加州大学电气与计算机工程系信号分析与解释实验室（SAIL））
