As networking systems become increasingly complex, achieving disruptive innovation grows more challenging. At the same time, recent progress in Large Language Models (LLMs) has shown strong potential for scientific hypothesis formation and idea generation. Nevertheless, applying LLMs effectively to networking research remains difficult for two main reasons: standalone LLMs tend to generate ideas by recombining existing solutions, and current open-source networking resources do not provide the structured, idea-level knowledge necessary for data-driven scientific discovery. To bridge this gap, we present SciNet, a research idea generation system specifically designed for networking. SciNet is built upon three key components: (1) constructing a networking-oriented scientific discovery dataset from top-tier networking conferences, (2) simulating the human idea discovery workflow through problem setting, inspiration retrieval, and idea generation, and (3) developing an idea evaluation method that jointly measures novelty and practicality. Experimental results show that \system consistently produces practical and novel networking research ideas across multiple LLM backbones, and outperforms standalone LLM-based generation in overall idea quality.

翻译：随着网络系统日益复杂，实现颠覆性创新愈发困难。与此同时，大型语言模型（LLM）的最新进展已展现出在科学假说形成与创意生成方面的巨大潜力。然而，将LLM有效应用于网络研究仍存在两大难题：独立LLM倾向于通过重组现有方案生成创意，而当前开源网络资源无法提供数据驱动科学发现所需的结构化、基于创意的知识。为弥合这一差距，我们提出专为网络研究设计的创意生成系统SciNet。SciNet基于三大核心组件构建：（1）从顶级网络会议构建面向网络的科学发现数据集，（2）通过问题设定、灵感检索与创意生成模拟人类创意发现流程，（3）开发联合评估新颖性与实用性的创意评价方法。实验结果表明，该系统能在多种LLM骨干网络上持续生成实用且新颖的网络研究创意，并在整体创意质量上超越基于独立LLM的生成方法。