Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems. FrontierOR includes 180 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, expert-verified evaluation suite. We evaluate seven LLMs spanning frontier, cost-effective, and open-source models both in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only 31% of cases in both solution quality and computational efficiency, and even strong coding agents with test-time evolution achieve only 50% on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward a feasible, high-quality, and efficient algorithm. Code and data are publicly released at https:\/\/github.com\/Minw913\/FrontierOR.<\/p>\n","protected":false},"excerpt":{"rendered":"
Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"name","value":"Minwei Kong","user_id":0},{"type":"name","value":"Chonghe Jiang","user_id":0},{"type":"name","value":"Ao Qu","user_id":0},{"type":"name","value":"Wenbin Ouyang","user_id":0},{"type":"name","value":"Zhaoming Zeng","user_id":0},{"type":"name","value":"Xiaotong Guo","user_id":0},{"type":"name","value":"Zhekai Li","user_id":0},{"type":"name","value":"Junyi Li","user_id":0},{"type":"name","value":"Yingying Fan","user_id":0},{"type":"name","value":"Xinshou Zheng","user_id":0},{"type":"name","value":"Xibin Jing","user_id":0},{"type":"name","value":"Yikai Zhang","user_id":0},{"type":"name","value":"Zhiwei Liang","user_id":0},{"type":"name","value":"Seong-Hee 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