超越目标等价：用约束注入法让大模型精准求解车辆路径优化问题，提升建模可靠性与实用性
arXiv:2606.04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into execut…
多智能体框架融合迭代修订与分层检索，攻克运筹学优化建模难题，开创新解法路径。
arXiv:2602.03318v3 Announce Type: replace Abstract: Operations Research (OR) relies on expert-driven modeling-a slow and fragile process ill-suited to…
提出语义锚点迭代校正方法，提升优化建模准确性与鲁棒性，获ICML 2026接收。
arXiv:2510.05115v3 Announce Type: replace Abstract: Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the ge…
Opt-Verifier通过双端验证机制，提升大语言模型构建数学优化模型的正确性，解决自动化建模中的关键难题。
arXiv:2605.29556v1 Announce Type: new Abstract: Building mathematical optimization models is critical in operations research (OR), while it requires s…
三重验证机制提升自动优化建模的可靠性，为AI辅助数学建模提供新思路。
arXiv:2605.23966v1 Announce Type: cross Abstract: Optimization modeling serves as the pivotal bridge between natural-language problem descriptions and…
这篇论文精准揭示了LLM在优化建模中的能力边界：纯文本推理随复杂度飙升鲁棒性崩溃，集成工具能算数却守不住全局约束，而自动公式化约束成为当前SOTA的核心瓶颈。一张结构化的路线图，指明下一代推理模型必须攻克的关卡。
arXiv:2601.19924v2 Announce Type: replace-cross Abstract: We investigate the capabilities and scalability of Large Language Models (LLMs) in optimizat…