arXiv:2606.18237v1 Announce Type: new Abstract: Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machi
The increasing complexity and opacity of AI models and research make reproducibility a pressing challenge, leading to new methods for evaluation.
Improving the reproducibility of AI research is critical for scientific integrity, reliable deployment, and accelerating AI development, impacting trust and efficiency across the AI ecosystem.
The introduction of scalable frameworks like ReproRepo could significantly reduce the manual effort and improve the accuracy of reproducibility audits in AI.
- · AI researchers
- · AI companies focused on reliable deployments
- · Open-source AI community
- · Academic institutions
- · Researchers with irreproducible methods
- · Organizations relying on unverified AI models
Increased emphasis on transparency and rigor in AI research and development.
Faster iteration and improvement of AI models due to better understanding of failure modes and dependencies.
Enhanced public and institutional trust in AI systems, leading to broader adoption and integration.
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