arXiv cs.AI· Yubo Wang, Rui Meng, Jaehyun Nam, Tomas Pfister, Jinsung Yoon·· 12 小时前AI 评分38
ReproBridge：为机器学习研究工件构建可复现性
ReproBridge: Building Reproducibility into ML Research Artifacts
AI 导读
ReproBridge 是一套智能体工作流，用于构建包含精炼代码库、环境配置、实验启动器和复现指南的标准化可复现工件。配套的 ReproBridge-Bench 覆盖 36 篇论文的 387 个实验共 1,029 项任务，在四个评测模型上把平均复现成功率从 76.4% 提升到 92.6%，下游 API 开销平均降低 19.7%。
正文 · 原文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.08452 [cs.AI] |
| (or arXiv:2611.08452v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08452 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yubo Wang [view email]
[v1]
Thu, 5 Nov 2026 18:45:36 UTC (3,856 KB)
来源：arXiv cs.AI · arxiv.org