From paper to partner: AI agents that answer questions and collaborate on research
Nature reports on Paper2Agent, a system that ingests a paper’s text, code, and data, hosts it on an MCP server, builds a paper-specific AI agent, and lets researchers interact in natural language; the agent can apply the paper’s methods to new data and even collaborate with agents from other disciplines. In tests on the AlphaGenome paper (DNA sequence predictors), the agent was created in about 45 minutes at a cost of ~$14 and achieved near-perfect accuracy on genetics questions, outperforming other biomedical AI agents and enabling reanalysis of conclusions without new experiments.