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Scaling Reproducibility: An AI-Assisted Workflow for Larg...
[Submitted on 17 Feb 2026 (v1), last revised 1 Jun 2026 (this ve · 2026-06-02 · via stat updates on arXiv.org

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Abstract: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 3,523 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the share of fully or largely reproducible papers rises from 20.8% before DA-RT adoption to 82.5% after, and conditional on accessible replication packages, 92.1% of papers are fully or largely reproducible (234/254). As a secondary application, we apply standardized IV diagnostics to 84 studies (597 IV specifications among 1,910 replicated models), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.

Submission history

From: Yiqing Xu [view email]
[v1] Tue, 17 Feb 2026 20:32:04 UTC (2,496 KB)
[v2] Wed, 25 Mar 2026 20:51:17 UTC (4,820 KB)
[v3] Mon, 1 Jun 2026 04:54:37 UTC (1,837 KB)