Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak attacks, and slow degradation of alignment in deployment. Building on a recent Second Law of Intelligence that treats ethical entropy as a state variable which tends to increase unless countered by alignment work, we make this framework operational for large language models. We define a five-way behavioral taxonomy, train a classifier to estimate ethical entropy S(t) from model transcripts, and measure entropy dynamics for base and instruction-tuned variants of four frontier models across stress tests. Base models show sustained entropy growth, while tuned variants suppress drift and reduce ethical entropy by roughly eighty percent. From these trajectories we estimate an effective alignment work rate gamma_eff and embed S(t) and gamma_eff in a monitoring pipeline that raises alerts when entropy drift exceeds a stability threshold, enabling run-time oversight of value drift.
Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Multi-Stream Environments
Australian Artificial Intelligence Institute (AAII)（澳大利亚人工智能研究所）；Faulty of Engineering and Information Technology（工程与信息技术学院）；University of Technology Sydney（悉尼技术大学）；Australia（澳大利亚）
TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift
Center for Data Science, New York University, New York, USA（数据科学中心，纽约大学，纽约，美国）；University of New Haven, Connecticut, USA（新罕布什尔大学，康涅狄格，美国）；Queens College, City University of New York, USA（皇后学院，纽约城市大学，美国）
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mila - Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；ETH Zurich（苏黎世联邦理工学院）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
School of Informatics, Xiamen University（厦门大学信息学院）；Tsinghua University（清华大学）；Xiamen Unisound Intelligence Technology Co., Ltd（厦门Unisound智能科技有限公司）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），中华人民共和国文化和旅游部，中国）
Unlocking the Pre-Trained Model as a Dual-Alignment Calibrator for Post-Trained LLMs
Department of Statistics and Data Science, Southern University of Science and Technology（统计与数据科学系，南方科技大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）；Department of Computer Sciences, University of Wisconsin-Madison（计算机科学系，威斯康星大学麦迪逊分校）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Path Drift in Large Reasoning Models:How First-Person Commitments Override Safety
The Second Affiliated Hospital, Guangdong Provincial Key Laboratory of Allergy and Clinical Immunology, Guangzhou Medical University（广东省过敏与临床免疫学重点实验室，广州医科大学第二附属医院）；NLP 2 CT Lab, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系NLP2CT实验室）
Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）；Yale University（耶鲁大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints
Hunan Normal University（湖南师范大学）；The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统重点实验室，中国科学院自动化研究所）；National University of Defense Technology（国防科技大学）
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
MARS 4.0 Fellowship, Cambridge AI Safety Hub(CAISH), University of Cambridge（剑桥大学）；AWS Generative AI Innovation Center, Amazon Web Services, USA（亚马逊网络服务）；Google, USA（谷歌）；Stanford University（斯坦福大学）；Northeastern University, Seattle, WA, USA（东北大学）
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；Tsinghua University（清华大学）；Nanyang Technological University（南洋理工大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；University of Chinese Academy of Sciences（中国科学院大学）
