一句话：提出 Artisan，将论文表格数值复现转化为 LLM 代码生成任务，并发布首个复现基准 Artisan-Bench。
可复现性是计算机科学研究的重要目标，但现有基于 LLM agent 的自动复现工作往往缺乏可执行证据，且未充分考虑复现方法。本文提出 Artisan：给定论文及其 artifact，自动生成可复现数值表格的代码。其核心贡献有两点：一是将复现问题形式化为代码生成任务，使用户能够独立审计并重新运行生成的复现脚本；二是设计自动评判机制，在不向 agent 泄露正确结果的同时引导其得到正确结果，并防止复制预计算结果的捷径。为评估该方法，作者构建了 Artisan-Bench，这是首个评估“生成复现研究结果代码”能力的基准，包含来自 23 篇软件工程论文的 60 个任务。实验表明 Artisan 有效且高效，并额外帮助发现了论文或 artifact 中的 20 个新错误。
Reproducibility is an important goal in computer science research, e.g., for artifact evaluation and to build upon experimental results of prior work. Recently, LLM agents are being used to automatically reproduce research results, but they fail to provide executable evidence of reproduction and do not consider the method of reproduction, which limits their usefulness. We present Artisan, an LLM agent that reproduces tables of numeric results, given a paper and its artifact. The approach is enabled by two key contributions: First, we frame the reproduction problem as a code generation task, enabling users to audit and re-run the resulting reproduction script independently of the agent. Second, we design automated judging mechanisms that steer the agent toward correct results without exposing them, while preventing shortcuts like copying precomputed results. To evaluate Artisan, we introduce Artisan-Bench, the first benchmark assessing the ability to generate code that reproduces research results. Artisan-Bench comprises 60 tasks derived from 23 software engineering papers. Our experiments show that Artisan is effective and efficient, with the added benefit of aiding the discovery of 20 new errors in either the paper or artifact.