What it is
The authors introduce Paper2Agent, an automated framework that converts a research paper and its codebase into an AI agent that acts as a virtual corresponding author. It uses multiple agents to analyse the paper and code, builds a model context protocol (MCP) server that exposes the paper's data, code and workflows as tools, then generates and runs tests to make that server more robust. In case studies, agents built around AlphaGenome, Scanpy and TISSUE reproduced the original papers' results and handled new user queries, and several agents collaborated to prioritize a causal gene for psoriasis.
Why it matters
Reusing published work normally means reading, understanding and adapting a paper's code, data and methods, a barrier that slows dissemination and reuse. Turning a paper into a tested, tool-invoking agent that a chat assistant can query in natural language changes it from a static artefact into something readers can run, which the authors frame as the basis for a collaborative ecosystem of AI co-scientists.
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Filed underBiomedical Text Mining and Ontologies, Scientific Computing and Data Management, AI in Service Interactions