系统梳理大模型在算法设计中的四大角色与应用进展
A Systematic Survey on Large Language Models for Algorithm Design
- 按优化器、预测器、提取器、设计者四类划分大模型作用
- 覆盖算法设计全阶段，涵盖组合优化到科学发现等应用
- 适合关注AI辅助算法创新的研究者与工程实践者
算法设计在各领域问题求解中至关重要。大语言模型（LLMs）的出现显著提升了该领域的自动化与创新能力，带来了全新视角与有前景的解决方案。短短几年间，这一融合已在组合优化、科学发现等领域取得显著进展。然而，由于缺乏系统性综述，当前研究仍受限于对领域的整体认知不足——现有综述或局限于特定子领域，或目标不同。本文旨在提供针对大模型用于算法设计的系统性回顾。我们提出一个分类体系，将大模型的角色归纳为优化器、预测器、提取器与设计者，并分析各类别下的进展、优势与局限。进一步地，我们整合了算法设计流程三个阶段的文献，以及多样化的算法应用场景，勾勒出当前研究图景。最后，我们指出了关键开放挑战与未来机遇，以指导后续研究。为支持未来研究与合作，我们提供了配套仓库：https://github.com/FeiLiu36/LLM4AlgorithmDesign。
原文摘要 · Abstract (English)
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.
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