Stanford's Paper2Agent turns research papers into queryable AI agents, Nature study says

Paper2Agent, published in Nature on Sept. 16 by Jiacheng Miao, James Zou and colleagues at Stanford University, uses several coordinated AI agents to read a paper and its code repository, package the methods as tools on a Model Context Protocol server, and then write and run tests on those tools. A chat agent connected to the server can apply the paper's methods to new data. The system was implemented with Anthropic's Claude Code.

According to the paper, an agent built from the AlphaGenome genomics paper took about 45 minutes and US$14 to create. It scored 98.7% on 15 tutorial-based queries and 100% on 15 new ones, ahead of Claude Code given the repository directly and the biomedical agent Biomni. Across 300 benchmark questions, paper agents averaged 91.2% accuracy against 80.3% for that baseline. Several agents working together also prioritized GPR137 as a candidate causal gene for psoriasis.

The 26 papers that failed conversion mostly lacked runnable code, data or working dependencies, the authors report, and they say hypothesis generation and interpretation still need human involvement. A preprint appeared on arXiv in September 2025.