Introduces Paper2Agent, an automated framework that converts research papers into interactive AI agents capable of answering questions and applying methods to new data.
Paper2Agent is an automated framework that transforms static research papers into interactive AI agents by converting manuscripts, code, and data into Model Context Protocol (MCP) servers. These agents function as virtual corresponding authors, enabling users to execute complex scientific analyses, reproduce original results, and collaborate with other paper agents through natural language queries. The system addresses technical barriers to dissemination by allowing researchers to apply methods to new data without manual code adaptation, as demonstrated in case studies involving AlphaGenome, Scanpy, and TISSUE.
Paper2Agent reframes research papers as interactive, executable knowledge artifacts rather than static documents. The work introduces an automated framework that converts a paper’s methodological content into an AI agent that can answer questions about the method, explain its assumptions, and apply the procedure to new data. This positions the paper not merely as a description of a technique, but as a reusable computational resource that can be queried, adapted, and operationalized by downstream users.
A key contribution is the shift from passive literature consumption to active method reuse. By grounding the agent in the source paper, the framework aims to make the resulting system both interactive and reliable: users can probe the method’s scope, inputs, limitations, and procedural steps, while the agent can help translate the described method into concrete applications. The broader insight is that much of the value in scientific literature lies in methods that are hard to reuse because they are embedded in prose, figures, and implicit context; Paper2Agent seeks to make that context machine-actionable.
The work matters because it addresses a persistent bottleneck in scientific practice: the gap between published methods and their practical reuse. If research papers can be systematically converted into trustworthy agents, researchers can reduce manual effort in method transfer, improve reproducibility, and accelerate cross-domain experimentation. More broadly, the paper points toward a future in which AI systems do not only summarize science, but help enact it—turning published knowledge into tools that can be tested, compared, and extended in new settings.