Code reproduction is a cornerstone of scientific validity, yet it remains a formidable challenge in computer networking research due to the scarcity of open-source implementations and the complexity of heterogeneous system architectures. While Large Language Models have demonstrated potential in code generation, existing code generation frameworks often fail to address the long-context constraints and intricate logical dependencies required to reproduce network systems from academic papers. To facilitate result reproduction, we introduce \emph{RepLLM}, an end-to-end multi-agent framework designed to automate the transformation of network research into executable code. RepLLM features a novel collaborative architecture comprising four specialized agents -- Content Parsing, Architecture Design, Code Generation, and Audit \& Repair -- coordinated through an explicit \textit{Shared Memory} mechanism to ensure global context consistency. With the enhancement of Chain-of-Thought LLM reasoning and a sandbox-isolated static-dynamic debugging methodology, our framework effectively resolves semantic discrepancies and runtime errors. Extensive evaluations on representative papers from SIGCOMM and NSDI demonstrate that RepLLM significantly outperforms state-of-the-art baselines in generating compile-ready and logically correct systems. Results further demonstrate that RepLLM facilitates the reproduction of 80\% of the original benchmarks with only four hours of human intervention.

翻译：代码复现是科学有效性的基石，但在计算机网络研究中，由于开源实现的稀缺性以及异构系统架构的复杂性，这仍然是一项艰巨的挑战。尽管大型语言模型在代码生成方面展现出潜力，但现有的代码生成框架往往无法满足从学术论文复现网络系统所需的长上下文约束和复杂的逻辑依赖关系。为了促进结果复现，我们引入了 \emph{RepLLM}，这是一个端到端多智能体框架，旨在将网络研究自动化转化为可执行代码。RepLLM 采用了一种新颖的协作架构，包含四个专用智能体——内容解析、架构设计、代码生成以及审计与修复——通过一个显式的 \textit{共享内存} 机制进行协调，以确保全局上下文的一致性。借助思维链 LLM 推理的增强以及沙盒隔离的静态-动态调试方法，我们的框架有效解决了语义差异和运行时错误。在 SIGCOMM 和 NSDI 代表性论文上进行的大量评估表明，RepLLM 在生成可直接编译且逻辑正确的系统方面显著优于现有最先进的基线方法。结果进一步证明，RepLLM 仅需四小时的人工干预即可复现 80\% 的原始基准测试。