// NATURE NEWS — SPAZIO & SCIENZA
Reimagining research papers as interactive and reliable AI agents
Nature
(2026) Cite this article
Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper’s code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent’s effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.
The research paper is the traditional unit of scientific communication. It remains the norm for documenting methods, results and insights, and is the primary way in which research is shared with the broader community. However, papers are fundamentally passive objects: a reader must discover the paper (not an easy task given the flood of publications), parse its contributions and manually determine how to apply them to their own work. In particular, when a paper describes a new computational method, substantial technical barriers often remain before the method can be used on new data4. A reader might need to locate the corresponding code repository, install dependencies, configure environments and interpret the correct inputs and outputs5. Even with well-maintained repositories, this process is often non-trivial.
For instance, consider AlphaGenome, which provides a powerful framework for genome-scale foundation modelling1. Despite its utility, this system requires substantial technical expertise to set up and deploy, limiting accessibility for biologists who could otherwise benefit. Using AlphaGenome in code involves installing the environment, creating client objects with application programming interface (API) keys, constructing inputs such as variant objects and selecting desired output modalities. Users must understand the API hierarchy and parameter semantics, which imposes a learning curve for biologists unfamiliar with these abstractions.
This illustrates a broader challenge: research outputs are passively siloed behind technical barriers. Paper2Agent reimagines research dissemination by turning static papers into active AI agents. Each agent serves as an interactive expert on the corresponding paper, capable of demonstrating, applying and adapting its methods to new projects.
AI agents are autonomous systems that can reason about tasks and act to achieve goals by leveraging external tools and resources6. Modern AI agents are typically powered by large language models (LLMs) connected to external tools or APIs, and can adapt based on feedback7. Notably, because agents are built on top of LLMs, users can interact with agents through human language, s