AI-Assisted Computational Reproducibility on the FABRIC Testbed

Researchers demonstrate that combining the FABRIC testbed with LLM-based coding assistants can significantly reduce the effort required to reproduce published scientific experiments. The AI-assisted approach achieved 4-6x reduction in reproduction effort across three case studies, though human intervention remained necessary for complex analytical workflows.

This research addresses a fundamental challenge in modern science: computational reproducibility. The study demonstrates a practical solution by leveraging AI assistants to automate routine tasks in experiment reproduction across diverse domains—from network congestion control to molecular dynamics simulations and genomics pipelines. The FABRIC testbed provides a standardized infrastructure for testing, while LLM assistants handle environment setup, code adaptation, and debugging, significantly accelerating the reproduction process.

The findings reveal both the capabilities and limitations of current AI technology. LLMs excel at environment configuration and code modification, where workflows are well-defined and problems have clear solutions. However, they struggle with analytical phases requiring domain expertise, creative problem-solving, and understanding of data dependencies. This suggests AI works best as a complement to human expertise rather than a replacement.

For the research community and scientific infrastructure providers, this work has immediate implications. Reproducibility crises across fields have undermined confidence in published results, and automating portions of reproduction could accelerate validation cycles. The 4-6x efficiency gain translates to faster peer review and more robust scientific publication standards.

Looking forward, the practical recommendations from this study could influence how research testbeds integrate AI tooling and how scientists approach experimental documentation. The research suggests future AI systems should be trained specifically on analytical workflows common in scientific computing, improving their ability to handle complex data dependencies and research-specific problem-solving.

• →AI-assisted workflows reduced computational reproducibility effort by 4-6 times across multiple scientific domains
• →LLM assistants excel at environment setup and code adaptation but require human guidance for complex analytical tasks
• →The FABRIC testbed combined with LoomAI demonstrates practical infrastructure for AI-enhanced scientific research validation
• →Reproducibility automation could accelerate peer review cycles and strengthen validation standards in scientific publishing
• →Current AI limitations in analytical reasoning suggest hybrid human-AI workflows remain optimal for research reproducibility