ReproBridge: Building Reproducibility into ML Research Artifacts
ReproBridge: Building Reproducibility into ML Research Artifacts
ReproBridge enhances the success rate of machine learning experiment replication through standardized design (refined codebase, environment setup, experiment launchers, and replication guides), increasing the average replication rate of four models from 76.4% to 92.6% on ReproBridge-Bench. Refined artifacts reduce the average downstream API cost by 19.7%, narrow the success rate gap between models to 3.9 percentage points, and achieve a success rate above 90% for all models. Removing code or environment components significantly reduces replication effectiveness.
Published on Nov 5
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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.
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