Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. In just a few years, this integration has yielded remarkable progress in areas ranging from combinatorial optimization to scientific discovery. Despite this rapid expansion, a holistic understanding of the field is hindered by the lack of a systematic review, as existing surveys either remain limited to narrow sub-fields or with different objectives. This paper seeks to provide a systematic review of algorithm design with LLMs. We introduce a taxonomy that categorises the roles of LLMs as optimizers, predictors, extractors and designers, analyzing the progress, advantages, and limitations within each category. We further synthesize literature across the three phases of the algorithm design pipeline and across diverse algorithmic applications that define the current landscape. Finally, we outline key open challenges and opportunities to guide future research. To support future research and collaboration, we provide an accompanying repository at: https://github.com/FeiLiu36/LLM4AlgorithmDesign.
@article{arxiv.2410.14716,
title = {A Systematic Survey on Large Language Models for Algorithm Design},
author = {Fei Liu and Yiming Yao and Ping Guo and Zhiyuan Yang and Zhe Zhao and Xi Lin and Xialiang Tong and Kun Mao and Zhichao Lu and Zhenkun Wang and Mingxuan Yuan and Qingfu Zhang},
journal= {arXiv preprint arXiv:2410.14716},
year = {2026}
}