7NRP: The Seventh National Research Platform Workshop, Part One The Seventh National Research Platform (7NRP) workshop was held at the University of California San... Reproducibility is absolutely critical in science, but it’s a troublesome characteristic when it comes to AI. Frontier models developed by Big AI may deliver superior accuracy and reasoning capabilities, but they do so largely as black boxes with little regard for reproducibility. If AI is going to turbo-charge scientific productivity, it must do so without compromising reproducibility. The question, then, becomes how to achieve it. This was the topic of a presentation at the TPC26 conference last week by Noah Smith, a computer scientist at the University of Washington and senior director of NLP research at the Allen Institute for Artificial Intelligence. Smith discussed why it’s important for scientists to have AI tools that meet their needs when it comes to reproducibility, and how model flows can help to deliver them. “Scientists need to be able to inspect and control their tools. A big part of science is your tools–the engineering, the systems that are going to help you answer questions,” Smith said. “At the Allen Institute for AI and with our collaborators at the University of Washington and other universities, we’ve taken the position that the way to get to this fine-grained control and inspectability is through what we call model flows.” What exactly is a “model flow”? Smith went on: “We use this term ‘model flow’ to refer to a kind of full openness,” he continued. “Everything that you need to reproduce the work from the very beginning: all of the data, the model weights…and intermediate checkpoints. We describe the entire recipe. I’ll give you all the code that you need to reproduce any stage so that you can go back and change anything.