做运筹优化或自动化建模的团队终于有了能应对问题类型变化的通用方案——OptSkills 通过原型聚类和技能蒸馏解决了传统方法对叙事变体敏感的问题，值得在复杂优化任务中试试。
OptSkills 是一种面向优化建模与求解的智能体系统，通过聚类问题原型而非表面叙事来提升泛化能力。它利用大语言模型自动从自然语言中提取优化问题，并在聚类内探索多样建模与求解配置，将成功轨迹蒸馏为可复用的工作流技能。在多个数据集上达到 68.27% 的微平均准确率，在 MIPLIB-NL 基准上以 26.91% 超越 DeepSeek-V3.2-Thinking 4.53%。该系统还支持在分布外场景下通过新轨迹扩展技能库，代码和技能已开源。
OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient paradigm for automated optimization. However, existing methods still exhibit limited generalization: they are sensitive to superficial narrative variations, reuse experience mainly at the case level, and struggle to adapt to shifted or emerging problem types. We propose OptSkills, an archetype-centric skill learning and reasoning agent system for optimization modeling and solving. To improve robust generalization, our system clusters problems by their underlying archetypes rather than surface narratives. To improve in-distribution generalization, it explores diverse modeling paradigms and solver configurations within each cluster, then distills successful trajectories into reusable workflow-level skills. To improve out-of-distribution generalization, it refines existing skills or expands the skill library using newly obtained trajectories. Our system achieves a state-of-the-art micro-averaged accuracy of 68.27% on datasets encompassing diverse problem types and scenarios. In addition, on MIPLIB-NL, a highly challenging large-scale and high-dimensional benchmark, it achieves 26.91% accuracy, outperforming DeepSeek-V3.2-Thinking by 4.53%. After skill learning on Nano-CO, it reaches 72.79% on the OOD NLCO benchmark. Code and skills are available at https://github.com/fujiwaranoM0kou/OptSkills.