Paper2Agent Turns Research Papers into AI Agents for Live Interaction
Paper2Agent is a new AI tool that turns a research paper into a dynamic, conversational agent. The system ingests a paper’s text, code, and data, stores it on an MCP server, and then uses autonomous agents to build tools that can apply the paper’s methods to new data. Scientists can connect to the server with any large‑language model and interact with the paper‑specific agent in plain language.
Key points
- Paper2Agent builds a paper‑specific AI agent in 45 minutes, costing US$14 compute
- The agent outperformed other biomedical AI agents like Biomni on the AlphaGenome paper
- It identified an alternative causal gene for high cholesterol, enabling re‑evaluation of published conclusions
In a test on the AlphaGenome paper, which predicts DNA sequence effects on gene expression, Paper2Agent built an agent in about 45 minutes at a compute cost of US$14. The agent answered genetics questions with near‑perfect accuracy and outperformed other biomedical agents such as Biomni. It even identified a different causal gene linked to high cholesterol, showing that the tool can re‑evaluate published conclusions without new experiments.
The ability to convert static papers into living, self‑applying agents could accelerate discovery, reduce duplication, and democratize access to cutting‑edge methods, reshaping how scientific knowledge is shared and validated.
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