Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable decisions in fine-grained and structurally complex industrial scenarios, and lack effective self-corrective mechanisms. To address this issue, we propose M3-AD, a unified reflection-aware multimodal framework for industrial anomaly detection. M3-AD comprises two complementary data resources: M3-AD-FT, designed for reflection-aligned fine-tuning, and M3-AD-Bench, designed for systematic cross-category evaluation, together providing a foundation for reflection-aware learning and reliability assessment. Building upon this foundation, we propose RA-Monitor, which models reflection as a learnable decision revision process and guides models to perform controlled self-correction when initial judgments are unreliable, thereby improving decision robustness. Extensive experiments conducted on M3-AD-Bench demonstrate that RA-Monitor outperforms multiple open-source and commercial MLLMs in zero-shot anomaly detection and anomaly analysis tasks. Code will be released at https://github.com/Yanhui-Lee/M3-AD.
Information School, the University of Sheffield（谢菲尔德大学信息学院）；Vamstar Ltd.（Vamstar有限公司）
While text mining and NLP research has been established for decades, there remain gaps in the literature that reports the use of these techniques in building real-world applications. For example, they typically look at single and sometimes simplified tasks, and do not discuss in-depth data heterogeneity and inconsistency that is common in real-world problems or their implication on the development of their methods. Also, few prior work has focused on the healthcare domain. In this work, we describe an industry project that developed text mining and NLP solutions to mine millions of heterogeneo…
Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education
R. B. Annis School of Engineering（R. B. Annis 工程学院）；University of Indianapolis（印第安纳大学）；E. S. Witchger School of Engineering（E. S. Witchger 工程学院）；Marian University（玛丽安大学）；University of Maryland Eastern Shore（马里兰大学东部分校）；The Boehm Center for Systems and Software Engineering（Boehm 系统与软件工程中心）
VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；State Key Laboratory of Intelligent Manufacturing System Technology（智能制造系统技术国家重点实验室）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机科学与人工智能学院）
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Software, Beihang University（北京航空航天大学软件学院）；Shandong Inspur Intelligent Production Technology Co., Ltd（山东浪潮智能生产技术有限公司）
Multi-Modal Fusion of In-Situ Video Data and Process Parameters for Online Forecasting of Cookie Drying Readiness
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校机械科学与工程系）；Department of Mechanical Engineering, University of Michigan（密歇根大学机械工程系）
Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT
Tongji University（同济大学）；The Hong Kong Polytechnic University（香港理工大学）；Guangdong Guodi Institute of Resources and Environment（广东国地资源环境研究院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；College of Design and Innovation, Tongji University（同济大学设计与创新学院）
IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
School of Cyber Engineering, Xidian University（电子科技大学信息工程学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯winburne技术大学科学与工程技术学院）；School of Computer Science and Technology, Xidian University（电子科技大学计算机科学与技术学院）
TS-MLLM: A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；School of Software, Beihang University（北京航空航天大学软件学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；State Key Laboratory of Intelligent Manufacturing System Technology（智能制造系统技术国家重点实验室）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Department of Computer Science, St. Francis Xavier University（圣弗朗西斯科大学计算机科学系）
FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学智能感知与机器人研究院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；Huakong AI Plus Company Limited（华冠AIplus有限公司）；Didi International Business Group（滴滴国际商务集团）
National Engineering Research Center for Big Data Software（国家大数据软件工程技术研究中心）；School of Software, Tsinghua University（清华大学软件学院）
Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. W…
Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
University of Macau（澳门大学）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；Tongji University（同济大学）；University of Southampton（南安普顿大学）；University of Bologna（博洛尼亚大学）；Minzu University of China（中央民族大学）
