ReproBridge 用标准化工件将 ML 实验复现成功率提升至 92.6%
ReproBridge: Building Reproducibility into ML Research Artifacts
搞 ML 复现的可以看看：一个 agent 工作流把论文代码整理成标准复现包，四个模型成功率都冲上 90%，API 花费还省了近两成。
ReproBridge 是一套构建可复现 ML 实验工件的 agent 工作流，产出包含精炼代码库、环境配置、实验启动器和复现指南的标准化工件。配套的 ReproBridge-Bench 包含来自 36 篇论文、387 个实验的 1029 个任务，每任务以 5 个人工验证的参考分数为锚点。在四个模型上，使用精炼工件后平均复现成功率从 76.4% 升至 92.6%，下游 API 开销降低 19.7%。跨模型成功率差距从 21.6 个百分点收窄到 3.9 个百分点，消融实验显示移除代码和环境组件损失最大。
ReproBridge: Building Reproducibility into ML Research Artifacts
Reproducing machine learning experiments requires reconstructing procedures from information scattered across papers, released code, and supporting artifacts. For coding agents, reproduction success can therefore depend on their ability to fill in missing details, rather than on the released artifacts alone. We ask whether preserving recovered procedures and decisions improves subsequent independent reproduction. We define a standardized artifact design comprising a refined codebase, environment setup, experiment launchers, and a reproduction guide, and introduce ReproBridge, an agent workflow for constructing these refined reproducible artifacts. ReproBridge-Bench provides 1,029 tasks from 387 experiments across 36 papers, anchored by five human-verified reference scores per task. Agents attempt tasks in fresh sessions with raw code or refined artifacts; a separate Auditor checks execution evidence. Across four evaluated models, refined artifacts raise mean reproduction success from 76.4% to 92.6% and reduce downstream API spending by 19.7% on average. The cross-model success-rate spread narrows from 21.6 to 3.9 percentage points, with all four models achieving mean success rates above 90%. Removing any component reduces success in the ablation study, with the largest losses from code and environment removal. These results suggest that preserving execution knowledge helps even models with lower raw-code success reproduce experiments more reliably and at lower API cost.