arXiv:2608.28725v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used as graders, verifiers, and process auditors, but most mathematical evaluations still emphasize final...
By Fateme Mazdarani, Carlos Toxtli
The paper examines large language models (LLMs) as tools for numerical optimization, focusing on their ability to exploit mathematical problem structures and generate near‑optimal solutions. An evaluation framework is introduced to separate solution quality from execution failures such as numerical inaccuracies, constraint violations, and formatting errors. Experiments on convex optimization, fractional programming, and constrained projection problems show that LLMs can capture useful optimization structures, but reliability decreases with higher dimensionality and depends on the problem’s structure. An iterative refinement mechanism that provides targeted feedback on feasibility and solution quality significantly improves reliability and extends the dimensional range where LLMs produce valid solutions, though persistent numerical and structural errors limit their use as standalone optimizers.
By Lingyun Guo, Ali Bereyhi
arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.
By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo
arXiv:2607. 10768v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed to solve optimization problems, yet existing benchmarks evaluate them on pre-structured mathematical formulations that bypass the most critical challenge: translating complex business requirements into correct models and solve efficiently.
By Yongchang Fu, Xinjie Huang, Chengjun Dai, Chengzhe Feng, Junshao Zhang, Hong Zhu
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:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.
By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu