Reproducing Papers to Code: RepLLM Multi-Agent Framework
jiqizhixin · x · 2026-08-09
Researchers from Xiamen University, Tsinghua University, and other institutions introduced RepLLM at SIGCOMM 2026. It is an automated multi-agent framework capable of turning networking research papers into compile-ready code.
Technical Highlights & Performance:
- Multi-Agent Collaboration: Four specialized AI agents collaborate to read papers, design system architectures, generate code, and debug it in an isolated sandbox.
- Shared Memory & Reasoning: Agents share a common memory for consistency and use structured reasoning to catch errors before they pile up.
- Results: RepLLM outperforms existing LLM code generation frameworks, reproducing 95% of original benchmarks in about 2 hours while cutting token usage by up to 10%.
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