发表机构
Chengdu University of Information Technology; Nanyang University of Technology; Shandong University; Singapore Management University; Eindhoven University of Technology（成都信息工程大学; 南洋理工大学; 山东大学; 新加坡管理大学; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对混合整数规划中的长周期自动研究，提出AutoMIP技能，通过思路池与算法树搜索管理实验，在MIPLib和MINLPLib上分别刷新31/60和52/60个实例的最优解。
AI 中文摘要
尽管自动研究近期取得了进展，但将其应用于实际的运筹学问题（通常表述为NP难的混合整数线性或非线性规划，即MILP或MINLP）仍然具有挑战性，因为有效的研究需要系统地管理相互竞争的思路和长周期的实验轨迹。我们引入了AutoMIP，一种可复用的智能体技能，通过思路池和算法树搜索来组织混合整数规划中的长周期自动研究。AutoMIP维护一个持久的互补候选思路池，同时将可执行的实验组织成算法树，使智能体能够保留未探索的假设，改进有前景的算法，并根据历史状态切换到替代的方法方向。在MILP和MINLP基准测试集上，AutoMIP在所评估的自动研究框架中取得了最高的最终成功率。在MIPLib上，AutoMIP为60个实例中的31个发现了新的最优解，超过了现有的自动研究框架。在MINLPLib上，它为60个实例中的52个实现了新的最优解。消融研究进一步证明了思路池和算法树搜索的互补贡献，强调了对于长周期自动研究，共同维护多样化的研究思路和结构化的实验轨迹的重要性。
英文摘要
Despite recent progress in autoresearch, applying it to practical operations research problems, typically formulated as NP-hard mixed-integer linear or nonlinear programs (MILPs or MINLPs), remains challenging because effective research requires systematically managing competing ideas and long-horizon experimental trajectories. We introduce AutoMIP, a reusable agent skill for organizing long-horizon autoresearch in mixed-integer programming through idea pooling and algorithm tree search. AutoMIP maintains a persistent pool of complementary candidate ideas while organizing executable experiments into an algorithm tree, enabling the agent to preserve unexplored hypotheses, refine promising algorithms, and switch to alternative methodological directions based on historical states. On MILP and MINLP benchmark cohorts, AutoMIP achieves the highest final success rates among the evaluated autoresearch frameworks. On MIPLib, AutoMIP discovers new best solutions for 31 of 60 instances, surpassing existing autoresearch frameworks. On MINLPLib, it achieves new best solutions for 52 of 60 instances. Ablation studies further demonstrate the complementary contributions of idea pooling and algorithm tree search, highlighting the importance of jointly maintaining diverse research ideas and structured experimental trajectories for long-horizon autoresearch.
