OptSkills is an archetype‑centric agent that learns and reasons about optimization problems using large language models. It clusters problems by underlying archetypes, explores diverse modeling and solver configurations within each cluster, and distills successful trajectories into reusable workflow‑level skills. The system achieves state‑of‑the‑art accuracy on multiple datasets, outperforming prior methods on challenging benchmarks such as MIPLIB‑NL and OOD NLCO.
By Haochen Yang, Ke Zhao, Mengyuan Ma, Xingyu Lu, Xiangfeng Wang, Hong Qian
arXiv:2510. 18428v4 Announce Type: replace Abstract: Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code.
By Minwei Kong, Ao Qu, Xiaotong Guo, Wenbin Ouyang, Chonghe Jiang, Han Zheng, Yining Ma, Dingyi Zhuang, Yuhan Tang, Junyi Li, Shenhao Wang, Haris Koutsopoulos, Hai Wang, Cathy Wu, Jinhua Zhao
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.
By Zhong Li, Xin Huang, Jinhui Wan, Xiangyi Wang, Shenkai Zhang, Ruiqi Chen, Wenyu Liu, Zaiwen Wen, Ziyan Luo
arXiv:2407. 19633v4 Announce Type: replace Abstract: Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.
By Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg, Shayan Talaei, Connor Lawless, Madeleine Udell
arXiv:2608.23353v1 Announce Type: new
Abstract: Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently s...
By Haofeng Yuan, Jianing Peng, Jieyi Bi, Ni Zhang, Shiji Song, Zhiguang Cao
arXiv:2607. 06974v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation.
By Ruilin Tong, Dong Gong
Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently shown promise in automated MIP modeling from natur...
Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation. When problems arrive sequentially, accumulating reusable experience across them can further improve performance.
arXiv:2608. 07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations.
By Shaofeng Zhang, Hongyuan Su, Qingwen Peng, Zefang Zong, Shengcai Liu, Ke Tang, Yong Li
arXiv:2609.08944v1 Announce Type: new
Abstract: Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality...
By Gaoyuan Li, Meihao Fan, Yizhe Liu, Shaolei Zhang, Ju Fan, Siyi Wang, Jiaheng Hou, Xudong Weng, Honghan Tian, Zang Li
arXiv:2607. 18256v1 Announce Type: new Abstract: Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code.
By Hongliang Lu, Zhong Li, Yuxuan Chen, Yuan Lan, Fan Zhang, Zaiwen Wen
arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.
By Roko Peran, Luka Hobor, Mihael Kovac, Mario Brcic