Reimagining research papers as interactive and reliable AI agents

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists. Paper2Agent converts research papers into interactive artificial intelligence agents by turning manuscripts, code and data into model context protocol-based tool-invoking systems that reproduce original results, answer new scientific queries and collaborate to generate novel insights.

• Two recent systems treat published papers as machine-actionable substrates rather than static text: Huang et al. 2025's Biomni mines tens of thousands of papers to extract tasks, tools and databases into a unified action space, while Miao et al. 2026's Paper2Agent converts individual papers into MCP-based agents validated to reproduce the original results and answer new queries. Trace →
• Two complementary strategies turn published literature into agent capability: Huang et al. 2025's Biomni mines tools, databases and protocols from tens of thousands of papers across 25 biomedical domains, while Miao et al. 2026's Paper2Agent converts individual papers into agents that function as virtual corresponding authors. Trace →

• AI for science and agentic research