Large Language Models (LLMs) are increasingly used as evaluators of reasoning quality, yet their reliability and bias in payments-risk settings remain poorly understood. We introduce a structured multi-evaluator framework for assessing LLM reasoning in Merchant Category Code (MCC)-based merchant risk assessment, combining a five-criterion rubric with Monte-Carlo scoring to evaluate rationale quality and evaluator stability. Five frontier LLMs generate and cross-evaluate MCC risk rationales under attributed and anonymized conditions. To establish a judge-independent reference, we introduce a consensus-deviation metric that eliminates circularity by comparing each judge's score to the mean of all other judges, yielding a theoretically grounded measure of self-evaluation and cross-model deviation. Results reveal substantial heterogeneity: GPT-5.1 and Claude 4.5 Sonnet show negative self-evaluation bias (-0.33, -0.31), while Gemini-2.5 Pro and Grok 4 display positive bias (+0.77, +0.71), with bias attenuating by 25.8 percent under anonymization. Evaluation by 26 payment-industry experts shows LLM judges assign scores averaging +0.46 points above human consensus, and that the negative bias of GPT-5.1 and Claude 4.5 Sonnet reflects closer alignment with human judgment. Ground-truth validation using payment-network data shows four models exhibit statistically significant alignment (Spearman rho = 0.56 to 0.77), confirming that the framework captures genuine quality. Overall, the framework provides a replicable basis for evaluating LLM-as-a-judge systems in payment-risk workflows and highlights the need for bias-aware protocols in operational financial settings.
AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation
School of Software Engineering, South China University of Technology（软件工程学院，华南理工大学）；School of Future Technology, South China University of Technology（未来技术学院，华南理工大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）
Financial Risk Assessment via Long-term Payment Behavior Sequence Folding
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Intelligent Computing Technology, Suzhou, CAS（中国科学院苏州智能计算技术研究所）；Tencent Weixin Group（腾讯微信集团）
AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents
New York University Abu Dhabi（纽约大学阿布扎比分校）；Nanyang Technological University（南洋理工大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；University of Sydney（悉尼大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National University of Singapore（新加坡国立大学）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）
TrueGradeAI: Retrieval-Augmented and Bias-Resistant AI for Transparent and Explainable Digital Assessments
Amity Center for Artificial Intelligence, Amity University（阿米蒂大学人工智能中心，阿米蒂大学）；Amity School of Engineering and Technology, Amity University（阿米蒂工程科技学院，阿米蒂大学）
AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment
Department of Computer Science, Sapienza University of Rome（计算机科学系，罗马萨皮恩扎大学）；Department of Computer, Control, and Management Engineering, Sapienza University of Rome（计算机、控制与管理工程系，罗马萨皮恩扎大学）；eZecute S.R.L.（eZecute公司）
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
Vrije University of Amsterdam（阿姆斯特丹自由大学）；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）；National Institute of Informatics（日本信息处理学会）；Shandong University（山东大学）；Guangzhou University（广州大学）
Distinct Profiles of Run-to-Run Score Reliability and Expert-Panel Alignment Across Four LLM Evaluators of Simulated Japanese-Language AI-to-AI Counseling
Japan National Institute of Occupational Safety and Health（日本国立职业安全卫生研究所）；Kaze To Taiyo（凯泽・太阳）；Saga Occupational Health Association（Saga职业健康协会）；Department of Pharmacy, Zikei Hospital/Zikei Institute of Psychiatry（药剂科，Zikei医院/Zikei精神医学研究所）；Department of Medical Welfare, Suzuka University of Medical Science（医疗福祉科， Suzuka医科大学）；Graduate School of Human Sciences, Ritsumeikan University（人类科学研究生院，立命馆大学）；Faculty of Nursing, National Defense Medical College（护理学部，国家防卫医疗大学）；Support Center for Students with Disabilities, Aoyama Gakuin University（残疾学生支持中心，上智大学）
Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge
National Institute of Informatics (NII)（国立信息学研究所）；University of Amsterdam（阿姆斯特丹大学）；University of Tokyo（东京大学）；Hitotsubashi University（一桥大学）；Centrum Wiskunde & Informatica (CWI)（荷兰数学与计算机科学研究中心）；Utrecht University（乌得勒支大学）
QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI
AI Lab, Arioobarzan Engineering Team（艾伊罗巴赞工程团队人工智能实验室）；Department of Computer Engineering and Information Technology（计算机工程与信息科技系）；Department of Informatics, Bioengineering, Robotics and Systems Engineering（信息学、生物工程、机器人与系统工程系）；University of Genoa（热那亚大学）
LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
dept. of Information Science University of North Texas Denton, Texas, USA（信息科学系 俄克拉荷马州立大学 丹顿 俄克拉荷马州 美国）；dept. of Data Science University of North Texas Denton, Texas, USA（数据科学系 俄克拉荷马州立大学 丹顿 俄克拉荷马州 美国）
Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
Department of Information Science, University of North Texas（北卡罗来纳州立大学信息科学系）；Department of Computer Science, North Carolina State University（北卡罗来纳州立大学计算机科学系）；Department of Data Science, University of North Texas（得克萨斯大学数据科学系）；School of Information Sciences, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校信息科学学院）
