We introduce Self-Anchoring Calibration Drift (SACD), a hypothesized tendency for large language models (LLMs) to show systematic changes in expressed confidence when building iteratively on their own prior outputs across multi-turn conversations. We report an empirical study comparing three frontier models -- Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.2 -- across 150 questions spanning factual, technical, and open-ended domains, using three conditions: single-turn baseline (A), multi-turn self-anchoring (B), and independent repetition control (C). Results reveal a complex, model-heterogeneous pattern that partially diverges from pre-registered hypotheses. Claude Sonnet 4.6 exhibited significant decreasing confidence under self-anchoring (mean CDS = -0.032, t(14) = -2.43, p = .029, d = -0.627), while also showing significant calibration error drift (F(4,56) = 22.77, p < .001, eta^2 = .791). GPT-5.2 showed the opposite pattern in open-ended domains (mean CDS = +0.026) with significant ECE escalation by Turn 5. Gemini 3.1 Pro showed no significant CDS (t(14) = 0.38, p = .710), but its Condition C data reveals a striking ECE pattern: without self-anchoring, Gemini's calibration error drops from .327 to near zero across repetitions, whereas self-anchoring holds ECE flat at approximately .333 -- indicating that SACD can manifest as suppression of natural calibration improvement rather than ac
The Art of Socratic Inquiry: A Framework for Proactive Template-Guided Therapeutic Conversation Generation
School of Information Science and Engineering, Lanzhou University（信息科学与工程学院，兰州大学）；School of Medical Technology, Beijing Institute of Technology（医学技术学院，北京理工大学）
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Center（新加坡国家眼科中心）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；National University of Singapore（新加坡国立大学）
From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
Tsinghua University（清华大学）；University of South California（南加州大学）；Qwen Team, Alibaba Group（通义实验室，阿里巴巴集团）；University of California San Diego（南加州大学圣地亚哥分校）；Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）
Empirical evidence of Large Language Model's influence on human spoken communication
Center for Humans and Machines（人类与机器中心）；Max-Planck Institute for Human Development（人类发展马克斯·普朗克研究所）；Center for Adaptive Rationality（适应性理性中心）；TUD Dresden University of Technology（德累斯顿技术大学）；Department of Business Analytics and Decision Science（商业分析与决策科学系）；Vienna University of Economics and Business（维也纳经济与商业大学）
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Fudan University（复旦大学）；The University of Melbourne（墨尔本大学）；Singapore Management University（新加坡国立大学）；Deakin University（德肯大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；City University of Hong Kong（香港城市大学）；University of Oxford（牛津大学）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；The University of Sydney（悉尼大学）；Griffith University（格里菲斯大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Sea AI Lab（Sea AI实验室）；Tsinghua University（清华大学）；Virginia Tech（弗吉尼亚理工大学）；CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全部）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）；Purdue University（普渡大学）；Duke University（杜克大学）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；RIKEN（理化学研究所）；The University of Tokyo（东京大学）
