REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research?

This repository contains the REPRO-Bench dataset, introduced in the paper REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research?.

REPRO-Bench is a novel benchmark designed to evaluate the capability of agentic AI systems in automating the assessment of social science paper reproducibility. It addresses limitations of existing benchmarks by providing a collection of 112 task instances, each representing a social science paper with a publicly available reproduction report. The benchmark features end-to-end evaluation tasks on reproducibility with complexity comparable to real-world assessments, encompassing diverse data formats and programming languages.

Official Codebase: https://github.com/uiuc-kang-lab/REPRO-Bench

📦 Dataset Structure

The REPRO-Bench dataset includes:

• 112 task instances (original paper PDFs + corresponding code and data)
• Gold-standard reproducibility annotations
• Public reproduction reports

🚀 Getting Started

The REPRO-Bench dataset is hosted on Hugging Face. To access and clone the dataset:

git clone https://huggingface.co/datasets/chuxuan/REPRO-Bench
cd REPRO-Bench
git lfs pull

To run the representative AI agents evaluated in the paper (for advanced usage, refer to the official codebase):

Run SWE-Agent

bash SWE-Agent/run_all.sh

Run AutoGPT

bash AutoGPT/classic/original_autogpt/run_all.sh

Run CORE-Agent

bash CORE-Agent/classic/original_autogpt/run_all.sh