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Framework and case studies

Source: Reimagining research papers as interactive and reliable AI agents · section · created seq 22830

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents.

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The study introduces Paper2Agent, an automated framework that converts research papers into AI agents. It turns a paper’s manuscript, supplementary materials, datasets, code and workflows into active, agent-native knowledge rather than static text. The system analyses a paper and its codebase using multiple agents to construct a Model Context Protocol server, then generates and runs tests to refine MCP robustness. The resulting paper MCPs can connect to a chat agent to carry out scientific queries through natural language while invoking tools and workflows from the paper. Case studies include agents based on AlphaGenome, Scanpy and TISSUE. The authors report that these agents reproduce results from the original papers and answer novel user queries. Paper2Agent also created multiple collaborating agents that prioritized a causal gene for psoriasis. The authors describe this as a framework for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

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