Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations
School of Computer Science, Wuhan University, China（武汉大学计算机科学学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China（香港科学与技术大学计算机科学与工程系）；Department of Computer Science（计算机科学系）
Code summaries are essential for helping developers understand code functionality and reducing maintenance and collaboration costs. Although recent advances in large language models (LLMs) have significantly improved automatic code summarization, the practical usefulness of generated summaries in industrial settings remains insufficiently explored. In collaboration with documentation experts from the industrial HarmonyOS project, we conducted a questionnaire study showing that over 57.4% of code summaries produced by state-of-the-art approaches were rejected due to violations of developers' expectations for industrial documentation. Beyond semantic similarity to reference summaries, developers emphasize additional requirements, including the use of appropriate domain terminology, explicit function categorization, and the avoidance of redundant implementation details.
To address these expectations, we propose ExpSum, an expectation-aware code summarization approach that integrates function metadata abstraction, informative metadata filtering, context-aware domain knowledge retrieval, and constraint-driven prompting to guide LLMs in generating structured, expectation-aligned summaries. We evaluate ExpSum on the HarmonyOS project and widely used code summarization benchmarks. Experimental results show that ExpSum consistently outperforms all baselines, achieving improvements of up to 26.71% in BLEU-4 and 20.10% in ROUGE-L on HarmonyOS. Furthermore, LLM-based evaluations indicate that ExpSum-generated summaries better align with developer expectations across other projects, demonstrating its effectiveness for industrial code documentation.
Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support
VIGOR: Verifiable Industrial Geometry Optimization and Repair in Scene Programs
Qilu University of Technology（齐鲁工业大学）；Tianjin University（天津大学）；Shandong University（山东大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；University of Chinese Academy of Sciences（中国科学院大学）；Shandong Computer Science Center（山东省计算中心）
LLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline
NSW Department of Education（新南威尔士州教育部）；South Australian Department for Education（南澳大利亚州教育部）；OC Selective exam preparation platform（OC精英考试备考平台）；Studitory: HSC preparation platform（Studitory: HSC备考平台）
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；University of South Carolina（南卡罗来纳大学）；IBM Yorktown（IBM约克镇分公司）；Indian AI Research Organization（印度人工智能研究组织）
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）
Scene Graph-Guided Generative AI Framework for Synthesizing and Evaluating Industrial Hazard Scenarios
Ph.D. Student, Dept. of Civil Eng., University of Texas at Arlington. E-mail；Assistant Professor, Dept. of Computer Sci. \& Eng., University of Texas at Arlington. E-mail；Assistant Professor, Dept. of Civil Eng., University of Texas at Arlington. E-mail
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Chongqing University（重庆大学）
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China（南方科技大学计算机科学与工程系，深圳，中国）；School of EEE, Nanyang Technological University (NTU), Singapore（南洋理工大学电子工程学院，新加坡）；CFAR, Agency for Science, Technology and Research (A*STAR), Singapore（科技研究局（A*STAR）的CFAR，新加坡）
