The Paper2Agent system could make it easier to understand papers, reproduce results, and generate hypotheses.

For centuries, the humble scientific paper has been the way researchers share discoveries. These papers are highly formulaic. Each starts with an introduction laying out the problem, followed by hypotheses, results, and conclusions.

More recently, journals have asked authors to include code or datasets—like brain scans or gene activity—for others to inspect and reuse. Video and image summaries are now often welcome too.

But for the most part, papers haven’t changed much. They may tell an intriguing story—if you can get past all the jargon. And a PDF dozens of pages long makes it tough to reproduce results, often the first step in a new project. Human error can also leave out important pieces.

“For years, we have felt that static research papers are not the best way to represent scientific knowledge,” wrote James Zou at Stanford University. His team is about to shake things up. This month, they described Paper2Agent, a workflow that turns papers into interactive AI agents.

These aren’t your average chatbots. Each is trained on a paper’s text, figures, and data, and asked to reproduce its results, essentially running through the experiments in a virtual world. This gives them a deeper understanding of the work, turning them into virtual “authors” that can answer complex questions about it. They can also apply methods from one paper to a new dataset and even collaborate with other paper agents across disciplines.

There’s now “an opportunity to fundamentally reimagine what knowledge looks like,” Zou said in a press release.

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