Paper2Agent is a system that turns research papers into interactive AI agents capable of explaining their own findings and reproducing their results. Instead of reading through dense academic text, you can ask an agent questions about what a paper actually says and have it walk you through the methodology and outcomes. The system has been published in Nature, which signals peer review validation. The tool lives at a live demo where you can interact with a Paper2Agent instance trained on the Paper2Agent paper itself—a recursive setup that lets you see the system in action immediately. The practical angle here is that reproducing scientific results is notoriously difficult. Papers often leave out implementation details, hyperparameters get lost in supplementary materials, and code may never be released. Paper2Agent addresses this by binding an AI agent directly to the paper's content and associated code or data. When you ask the agent a question, it can pull from both the written text and executable workflows, then actually run experiments to show you the results rather than just describing them. If you want to build your own Paper2Agent workflows, the team has made the code available on GitHub. This isn't locked behind a paywall or a proprietary platform—it's open source. The combination of a published Nature paper, a working demo, and released code suggests they're serious about making this reproducible and usable rather than just announcing a concept.