Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai University of Finance and Economics（上海财经大学）；Technische Universität München（慕尼黑技术大学）；Southwestern University of Finance and Economics（西南财经大学）
Yongzi Yu, Ao Li, Le Wang, Ziyue Li, Fugee Tsung, Yuxuan Liang, Man Li
Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a "Plan First, Judge Later" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.
Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan
Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback
School of Electrical Engineering and Computer Science（电气工程与计算机科学学院）
Beyond Correctness: Enhancing Architectural Reasoning in Code LLMs via Scalable Labeling with Agentic Judgment
Centre for Software Excellence, Huawei Canada（华为加拿大软件卓越中心）；Department of Computer Science, University of Manitoba, Canada（曼尼托巴大学计算机科学系）；School of Computing, Queen’s University, Canada（皇后大学计算科学学院）
AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，地点，国家）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家）；University of Delaware, Newark, DE, USA（特拉华大学）
DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA（机械工程系，密歇根大学，安阿伯，密歇根州，美国）；Max-Planck-Institute for Sustainable Materials, Materials Informatics, Düsseldorf, Germany（可持续材料研究所，材料信息学，杜塞尔多夫，德国）；Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA（机械工程，密歇根大学，安阿伯，密歇根州，美国）
When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
Department of Physics, CCTP；ITCP, University of Crete, 71303, Greece；Centre for Theoretical Physics, Department of Physics；Astronomy, Queen Mary University of London, London E1 4NS, United Kingdom；Theoretical Physics Department, CERN, Geneva, Switzerland
Position: Safety and Fairness in Agentic AI Depend on Interaction Topology, Not on Model Scale or Alignment
Department of Computer Science, University of British Columbia, Vancouver, Canada（英属哥伦比亚大学计算机科学系）；Department of Artificial Intelligence, IIT Hyderabad, Hyderabad, India（印度海得拉巴理工学院人工智能系）；Amazon, Delhi, India（印度德里亚马逊公司）
LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework
Department of Computer Science and Engineering, 2 Division of Medicinal Chemistry & Pharmacognosy, 3 Department of Biomedical Informatics, 4 Translational Data Analytics Institute（1 计算机科学与工程系，2 药物化学与药理学系，3 生物医学信息学系，4 转化数据分析研究所）
