Large Language Models (LLMs) have achieved strong performance on static reasoning benchmarks, yet their effectiveness as interactive agents operating in adversarial, time-sensitive environments remains poorly understood. Existing evaluations largely treat reasoning as a single-shot capability, overlooking the challenges of opponent-aware decision-making, temporal constraints, and execution under pressure. This paper introduces Strategic Tactical Agent Reasoning (STAR) Benchmark, a multi-agent evaluation framework that assesses LLMs through 1v1 zero-sum competitive interactions, framing reasoning as an iterative, adaptive decision-making process. STAR supports both turn-based and real-time settings, enabling controlled analysis of long-horizon strategic planning and fast-paced tactical execution within a unified environment. Built on a modular architecture with a standardized API and fully implemented execution engine, STAR facilitates reproducible evaluation and flexible task customization. To move beyond binary win-loss outcomes, we introduce a Strategic Evaluation Suite that assesses not only competitive success but also the quality of strategic behavior, such as execution efficiency and outcome stability. Extensive pairwise evaluations reveal a pronounced strategy-execution gap: while reasoning-intensive models dominate turn-based settings, their inference latency often leads to inferior performance in real-time scenarios, where faster instruction-tuned models prevail. These results show that strategic intelligence in interactive environments depends not only on reasoning depth, but also on the ability to translate plans into timely actions, positioning STAR as a principled benchmark for studying this trade-off in competitive, dynamic settings.
Assessing the Code Clone Detection Capability of Large Language Models
School of Computer Science, University of Galway（戈尔韦大学计算机科学学院）；CRT-AI, the Science Foundation Ireland Centre for Research Training in Artificial Intelligence（爱尔兰科学基金会人工智能研究培训中心（CRT-AI））；Lero, the Science Foundation Ireland Research Centre for Software（爱尔兰科学基金会软件研究中心（Lero））
A Systematic Approach for Assessing Large Language Models' Test Case Generation Capability
R. B. Annis School of Engineering（R.B.安尼斯工程学院）；E. S. Witchger School of Engineering（E.S.威彻格工程学院）；University of Indianapolis（印第安纳波利斯大学）；Marian University（玛丽安大学）
ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketing
Systems Engineering Institute, Xi’an Jiaotong University（系统工程研究所，西安交通大学）；State Key Laboratory of Multiphase Flow in Power Engineering, School of Energy and Power Engineering, Xi’an Jiaotong University（电力工程多相流国家重点实验室，能源与动力工程学院，西安交通大学）；School of Electronic Science and Engineering, Xi'an Jiaotong University（电子科学与工程学院，西安交通大学）；Organizational Management Department, School of Management, Xi’an Jiaotong University（组织管理部，管理学院，西安交通大学）
Crisis-Bench: Benchmarking Strategic Ambiguity and Reputation Management in Large Language Models
Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Baptist University（香港 Baptist 大学）；National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国理工学院）
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National Health Data Institute, Shenzhen（深圳国家健康数据研究院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors
School of Computer Science, Peking University（北京大学计算机科学学院）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）；Department of Computer Science, Yale University（耶鲁大学计算机科学系）；School of Psychological and Cognitive Sciences, Peking University（北京大学心理学与认知科学学院）
EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-commerce Models
Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）；Translational Data Analytics Institute, The Ohio State University（俄亥俄州立大学转化数据分析研究所）；Department of Biomedical Informatics, The Ohio State University（俄亥俄州立大学生物医学信息学系）
Tecnológico de Monterrey（蒙特雷科技大学）；University of Oxford（牛津大学）
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and annotated datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time (5 frames-per-second (fp…
