Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions

2025-07-02

AI Summary

Generative AI (GenAI) exhibits fundamental limitations in network optimization—namely, restricted theoretical generalization capacity, weak constraint satisfaction, absence of conceptual understanding, and inherently probabilistic outputs. Method: We propose a novel taxonomy of network optimization problems, distinguishing between one-shot optimization and Markov decision processes (MDPs). For both settings, we derive the first tight generalization bounds for generative diffusion models and large language models. Through rigorous theoretical analysis and critical comparative study, we characterize the intrinsic misalignment between generative objectives and optimization objectives. Contribution/Results: This work establishes the first systematic characterization of GenAI’s capability boundaries in network optimization, debunks the misconception that “universal generation implies universal optimization,” identifies core technical challenges—including constraint fidelity and solution determinism—and provides foundational insights for developing trustworthy, optimization-aware network intelligence.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Generation
• Machine Learning: Optimization

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Examines critical limitations of generative AI in network optimization
• Analyzes GenAI paradigms: diffusion models and pre-trained models
• Highlights overestimated capabilities and key constraints of GenAI

Innovation

• Generative diffusion models for network optimization
• Large pre-trained models in networking tasks
• Theoretical generalization bounds for GDMs