Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication -- retrieving materials, reconstructing environments, executing code, and matching outputs to point estimates reported in regression tables. We define a universe of all empirical and quantitative papers from the three top political science journals (2010--2025) and measure stated data availability using automated extraction. For a stratified sample of 384 studies, we apply the workflow to conduct full-paper replication, totaling 3,382 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the full-paper reproducibility rate rises from 29.6% before DA-RT adoption to 79.8% after, and conditional on accessible replication packages, 94.4% of papers are fully reproducible (237/251). As a secondary application, we apply standardized IV diagnostics to 92 studies (215 specifications), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.
@article{arxiv.2602.16733,
title = {Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Replication and Reanalysis},
author = {Yiqing Xu and Leo Yang Yang},
journal= {arXiv preprint arXiv:2602.16733},
year = {2026}
}