While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and organize our discussion around a categorization we introduce, dividing network optimization problems into two primary formulations: one-shot optimization and Markov decision process (MDP). We first trace key works, including foundational contributions from the AI community, and categorize current efforts in network optimization. We also review frontier applications of GDMs and LPTMs in other networking tasks, providing additional context. Furthermore, we present theoretical generalization bounds for GDMs in both one-shot and MDP settings, offering insights into the fundamental factors affecting model performance. Most importantly, we reflect on the overestimated perception of GenAI's general capabilities and caution against the all-in-one illusion it may convey. We highlight critical limitations, including difficulties in constraint satisfying, limited concept understanding, and the inherent probabilistic nature of outputs. We also propose key future directions, such as bridging the gap between generation and optimization. Although they are increasingly integrated in implementations, they differ fundamentally in both objectives and underlying mechanisms, necessitating a deeper understanding of their theoretical connections. Ultimately, this survey aims to provide a structured overview and a deeper insight into the strengths, limitations, and potential of GenAI in network optimization.

翻译：尽管近年来生成式人工智能在网络优化中的应用兴趣激增，但其快速发展往往掩盖了生成模型固有的关键局限性，而这些局限性在现有文献中仍未得到充分审视。本文对生成式人工智能在网络优化中的应用进行了全面的回顾与批判性分析。我们聚焦于生成式人工智能的两大主导范式，包括生成扩散模型和大规模预训练模型，并围绕我们引入的一种分类方法组织讨论，将网络优化问题划分为两种主要形式：一次性优化和马尔可夫决策过程。我们首先追溯了关键研究工作，包括来自人工智能社区的基础性贡献，并对当前网络优化领域的努力进行了分类。我们还回顾了生成扩散模型和大规模预训练模型在其他网络任务中的前沿应用，以提供更多背景信息。此外，我们提出了生成扩散模型在一次性优化和马尔可夫决策过程两种设置下的理论泛化界，为理解影响模型性能的根本因素提供了见解。最重要的是，我们反思了对生成式人工智能通用能力的高估，并警示其可能传达的"万能"错觉。我们强调了其关键局限性，包括满足约束条件的困难、有限的概念理解能力以及输出固有的概率性本质。我们还提出了关键的未来研究方向，例如弥合生成与优化之间的鸿沟。尽管它们在实现中日益融合，但两者在目标和底层机制上存在根本差异，需要对其理论联系进行更深入的理解。最终，本文旨在提供一个结构化的概述，并更深入地洞察生成式人工智能在网络优化中的优势、局限性和潜力。