As intelligent network services continue to diversify, ensuring efficient and adaptive resource allocation in edge networks has become increasingly critical. Yet the wide functional variations across services often give rise to new and unforeseen optimization problems, rendering traditional manual modeling and solver design both time-consuming and inflexible. This limitation reveals a key gap between current methods and human solving - the inability to recognize and understand problem characteristics. It raises the question of whether problem-aware learning can bridge this gap and support effective cross-problem generalization. To answer this question, we propose a problem-aware diffusion (PAD) model, which leverages a problem-aware learning framework to enable cross-problem generalization. By explicitly encoding the mathematical formulations of optimization problems into token-level embeddings, PAD empowers the model to understand and adapt to problem structures. Extensive experiments across ten representative network optimization problems show that PAD generalizes well to unseen problems while avoiding the inefficiency of building new solvers from scratch, yet still delivering competitive solution quality. Meanwhile, an auxiliary constraint-aware module is designed to enforce solution validity further. The experiments indicate that problem-aware learning opens a promising direction toward general-purpose solvers for intelligent network operation and resource management. Our code is open source at https://github.com/qiyu3816/PAD.

翻译：随着智能网络服务的持续多样化，确保边缘网络中高效自适应的资源分配变得日益关键。然而，服务间广泛的功能差异常常引发新的、不可预见的优化问题，使得传统的手工建模与求解器设计既耗时又缺乏灵活性。这一局限揭示了当前方法与人类求解能力之间的关键差距——无法识别和理解问题特征。这引出了一个核心问题：问题感知学习能否弥合这一差距，并支持有效的跨问题泛化？为回答此问题，我们提出了一种问题感知扩散（PAD）模型，该模型利用问题感知学习框架实现跨问题泛化。通过将优化问题的数学公式显式编码为令牌级嵌入，PAD使模型能够理解并适应问题结构。在十个代表性网络优化问题上的大量实验表明，PAD对未见问题具有良好的泛化能力，同时避免了从头构建新求解器的低效性，且仍能提供有竞争力的求解质量。此外，我们还设计了一个辅助的约束感知模块以进一步增强解的有效性。实验表明，问题感知学习为智能网络运营与资源管理的通用求解器开辟了有前景的研究方向。我们的代码已在 https://github.com/qiyu3816/PAD 开源。