Paper2Agent: Converting Research Papers into Interactive AI Agents for Scientific Discovery
By
Gaishan
A respectable bake. You'd come back tomorrow for another.
Summary
Paper2Agent is an automated framework that converts research papers into interactive AI agents, transforming static research outputs into active systems that can accelerate scientific discovery and adoption. The system analyzes papers and associated codebases using multiple agents to create Model Context Protocol (MCP) servers, then iteratively tests and refines them. These paper agents can be connected to chat interfaces like Claude Code to handle complex scientific queries through natural language while executing tools and workflows from the original research. The framework has been validated through case studies in genomics and transcriptomics, demonstrating its ability to reproduce original paper results and handle novel user queries.
Key quotes
· 5 pulledPaper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery.
Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse.
By turning static papers into dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for knowledge dissemination and a foundation for the collaborative ecosystem of AI co-scientists.
Paper2Agent created an agent that leverages AlphaGenome to interpret genomic variants and agents based on ScanPy and TISSUE to carry out single-cell and spatial transcriptomics analyses.
These paper MCPs can then be flexibly connected to a chat agent to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper.
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