Scientific papers can transform into AI agents that, according to the Stanford team behind the project, should speed up the dissemination of new scientific discoveries.
Paper2Agent, the team's new framework described in a paper published in Nature on Wednesday, converts scientific papers and their associated research outputs into agents that can discuss a paper’s findings, reproduce analyses and results, apply its methods to new data, and even collaborate with other paper agents on new research problems.
“Papers have been static documents for centuries,” James Zou, a Stanford computer scientist and biomedical data science professor and one of the paper’s authors, said in a LinkedIn post announcing P2A’s publication.
Giving a large language model access to a scientific paper is unpredictable, Zou added. What his team wanted was an agent that could act as a “virtual author” that had hands-on experience with a paper’s work, not just reading it and trying to understand it.
What that means in practice, as explained in the paper, is a workflow that uses a paper and its associated data, repository, and codebase to create a Model Context Protocol server exposing the research's tools, resources, and workflows. An LLM agent can then connect to the server and use natural-language requests to autonomously run demonstrations, reproduce analyses, apply a paper's methods to new data, and the like.
According to the paper, the MCP server itself can be hosted remotely, but Zou explained to The Register in an email that it can also be run locally to protect sensitive information, though such info will still be sent to whichever LLM backend P2A is connected to.