Paper2Agent 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. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant.
Each agent serves as an interactive expert on the corresponding paper, capable of demonstrating, applying, and adapting its methods to new projects.
It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (MCP) server, then iteratively generates and runs tests to refine and robustify the resulting MCP. These paper MCPs can then be flexibly connected to a chat agent (Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper.
They demonstrate Paper2Agent’s effectiveness in creating reliable and capable paper agents through in-depth case studies. 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. They validate that these paper agents can reproduce the original paper’s results and can correctly carry out novel user queries. 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.
Each Scientific Field Should Be A Collection of Many Agents
While the paper is the conventional unit of scientific communication, it is not always the best unit for agentification. In many fields, an idea
evolves across a sequence of publications, each adding refinements, benchmarks, or applications. In such cases, the most useful agent may not represent a single paper but rather a collection of related works aggregated into a coherent interface. A single MCP can encapsulate multiple related papers. They plan to work on extensions of Paper2Agent to flexibly accommodate this broader scope.
All Papers and Research Should Have Agent Availability
Looking forward, just as many journals now require data and code availability sections, they anticipate the emergence of an “agent availability” section that specifies whether and how the contribution has been embodied as an interactive agent. This would not only provide immediate utility to readers but also incentivize authors to present their work in a form conducive to agentification.
Linking Agents
Once scientific knowledge is encoded in active agents rather than static artifacts, the potential extends beyond individual use. Agents could interact with one another, linking methods to datasets or combining insights from different domains. For example, an agent representing a new analytical method could collaborate with an agent representing a newly released dataset, jointly producing analyses that neither artifact could support alone. Communities of such agents could form a dynamic, interoperable layer of scientific intelligence, accelerating connections across disciplines. Paper2Agent thus points toward a future in which scientific communication is not only about describing results, but also about creating interactive, collaborative entities that embody
and extend the research.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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