Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation awareness almost entirely in English, although models serve users in many languages. Indic languages are spoken by over a billion people, yet evaluation awareness remains almost entirely unstudied in them. We test four instruction-tuned models from three families in five Indic languages alongside English. A set of cues varies whether the model is told that it is monitored and whether the monitoring is a safety test. Models refuse harmful requests more often when they are told that they are being tested. This increase appears in all four models and all six languages. In Indic languages, refusal of harmful requests rises by 14.4 percentage points on average. The test cue also brings Indic refusal of harmful requests level with English, but raises refusal of benign Indic requests from 6.9% to 11.3%. We release all 47,880 judged responses to support further work. These results suggest that safety evaluations in Indic languages may not reflect how models treat their everyday users.
University of Cambridge（剑桥大学）；ELLIS Institute Tübingen（ELLIS研究所图宾根）；MPI-IS（马克斯·普朗克智能系统研究所）；Tübingen AI Center（图宾根人工智能中心）；Mila（Mila魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）
Constellation University of Wisconsin-Madison（威斯康星大学麦迪逊分校星座研究所）；Constellation Georgia Institute of Technology（佐治亚理工学院星座研究所）；UK AI Security Institute（英国人工智能安全研究所）
Large Language Models Lack Temporal Awareness of Medical Knowledge
University of Virginia（弗吉尼亚大学）；National Institutes of Health（美国国家卫生研究院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Yale University（耶鲁大学）；Weill Cornell Medicine（韦氏 Cornell 医学院）
Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B
Sparkverse AI Ltd（Sparkverse AI有限公司）；Department of Informatics and Systems（信息学与系统系）；University of Management and Technology（管理与技术大学）；Faculty of Information Technology and Computer Science（信息科技与计算机科学学院）；University of Central Punjab（旁遮普大学）
The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models
University College London（伦敦大学学院）；Queen’s University Belfast（贝尔法斯特女王大学）；The University of Tokyo（东京大学）；University of Bisha（比沙大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；AI Centre, University College London（伦敦大学学院人工智能中心）
Large Vision-Language Models as Emotion Recognizers in Context Awareness
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Cognition and Intelligent Technology Laboratory（认知与智能技术实验室）；Engineering Research Center of AI and Robotics（人工智能与机器人工程研究中心）；AI and Unmanned Systems Engineering Research Center of Jilin Province（吉林省人工智能与无人系统工程研究中心）
University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Charles Sturt University（查尔斯特大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；University College of Goizha（戈伊扎大学学院）；Ramco Institute of Technology（拉姆科理工学院）；Guru Nanak Dev University（古鲁那纳克德夫大学）；King Fahd University of Petroleum & Minerals（法赫德国王石油与矿产大学）；Charmo University（查尔莫大学）；Sulaimani Polytechnic University（苏莱曼尼理工大学）
Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems…
EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；AI Lab, Tencent（腾讯AI实验室）；Robotics X, Tencent（腾讯Robotics X实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）
