Optimization modeling via mixed-integer linear programming (MILP) is fundamental to industrial planning and scheduling, yet translating natural-language requirements into solver-executable models and maintaining them under evolving business rules remains highly expertise-intensive. While large language models (LLMs) offer promising avenues for automation, existing methods often suffer from low data efficiency, limited solver-level validity, and poor scalability to industrial-scale problems. To address these challenges, we present EvoOpt-LLM, a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling, including automated model construction, dynamic business-constraint injection, and end-to-end variable pruning. Built on a 7B-parameter LLM and adapted via parameter-efficient LoRA fine-tuning, EvoOpt-LLM achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples. The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of ~0.56 on medium-sized LP models with only 400 samples. EvoOpt-LLM demonstrates a practical, data-efficient approach to industrial optimization modeling, reducing reliance on expert intervention while improving adaptability and solver efficiency.
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）
A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing
The Hong Kong Polytechnic University（香港理工大学）；Kuaishou Technology（快手科技）；University of Electronic Science and Technology of China（电子科技大学）；Southwest Jiaotong University（西南交通大学）
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University（上海数据科学 key laboratory，计算机科学学院，复旦大学）；School of Management, Fudan University（管理学院，复旦大学）；Tencent Weixin Group（腾讯微信集团）；IFM Lab, University of California, Davis（加州大学戴维斯分校IFM实验室）
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
MetaMind Innovations P.C.（MetaMind创新公司）；Kingston University（金斯顿大学）；University of Western Macedonia（西马其顿大学）；Aristotle University of Thessaloniki（亚里士多德大学塞萨洛尼基分校）；University of Peloponnese（伯罗奔尼撒大学）；K3Y Ltd.（K3Y有限公司）
