RepLLM uses four agents to reproduce network-system papers in about two hours
机器之心 · wechat · 2026-07-28
RepLLM is a multi-agent framework for automatically reproducing network research results from papers and data.
It breaks the task into four stages: paper parsing, system architecture design, code generation, and audit/repair. The system uses shared memory across agents so the paper knowledge, interfaces, code state, and fixes stay consistent.
In seven representative network-system tasks — including NCFlow, GRooT, RatelessIBLT, SelfStarter, NetDice, Teal, and NetChain — the generated code was runnable and loaded the target datasets. With limited human calibration, the team reports full reproduction in roughly 82 to 138 minutes, compared with weeks of manual effort in traditional workflows.
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