Reproduce “BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge” on OpenResearch
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About this paper
Sihan Fu, Oucheng Liu, Shiyuan Wang, Jin Shi, Chengkun Wei
BootstrapAgent, a multi-agent framework, distills repository setup procedures into a reusable, verifiable ".bootstrap" contract. This system achieved a 92.9% success rate in generating functional setups and significantly reduced the cost for downstream AI code agents, cutting median token usage by 41% and wall-time by 28% for repository bootstrapping.
Key insights
- Formulating repository bootstrapping as a reusable knowledge problem, codified in a verifiable contract, significantly amortizes the setup cost and reduces redundant effort for AI agents.
- A two-level correctness mechanism involving "warm repair with clean replay" is crucial for balancing efficient iterative debugging during contract generation with guaranteed cold-start reproducibility.
- Trace-driven repair, guided by a persistent plan and incorporating a "sanity check" to prevent agents from weakening verification commands, is essential for robustly synthesizing and refining trustworthy setup contracts.
Summary by alphaXiv.
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