For centuries, the humble scientific paper has been the way researchers share discoveries. These papers are highly formulaic. Each starts with an introduction laying out the problem, followed by hypotheses, results, and conclusions.

More recently, journals have asked authors to include code or datasets—like brain scans or gene activity—for others to inspect and reuse. Video and image summaries are now often welcome too.

But for the most part, papers haven’t changed much. They may tell an intriguing story—if you can get past all the jargon. And a PDF dozens of pages long makes it tough to reproduce results, often the first step in a new project. Human error can also leave out important pieces.

“For years, we have felt that static research papers are not the best way to represent scientific knowledge,” wrote James Zou at Stanford University. His team is about to shake things up. This month, they described Paper2Agent, a workflow that turns papers into interactive AI agents.

These aren’t your average chatbots. Each is trained on a paper’s text, figures, and data, and asked to reproduce its results, essentially running through the experiments in a virtual world. This gives them a deeper understanding of the work, turning them into virtual “authors” that can answer complex questions about it. They can also apply methods from one paper to a new dataset and even collaborate with other paper agents across disciplines.

There’s now “an opportunity to fundamentally reimagine what knowledge looks like,” Zou said in a press release. “Instead of having only passive artifacts, why don’t we convert each static record into an active embodiment of knowledge?”

Embarrassment of Riches

The volume of scientific publishing has skyrocketed in recent decades. Global scientific output topped three million papers in 2023, and the numbers are still climbing.

That much literature would overwhelm anyone. Yet reading papers to build up foundational knowledge is the first step in any scientific project. The challenge is even thornier when research crosses disciplines—say AI and protein science, or machine consciousness and neuroscience. In these cases, scientists must master multiple fields, but the connections they form often spark breakthroughs.

The sheer volume creates another headache: reproducibility. Scientists are a critical bunch. Before building on a previous paper’s conclusions, they often try to recreate the experiment to see if they get the same results.

It doesn’t always work. In the landmark 2015 Reproducibility Project, only 39 percent of psychology studies produced results consistent with their original findings. A 2026 analysis didn’t fare much better. And large language models may add a wrinkle in machine learning research, where the pipeline, from data collection to model selection and training, isn’t always well documented.

For Zou and team, turning papers into AI agents could help us tackle these problems.

‘Living’ Knowledge

To develop Paper2Agent, the team first ask it to scan all sections of a paper and then try to recreate its results. Along the way, it captures details that a reader might otherwise have to dig out, such as experimental setups and chemicals, and saves them in a digital vault called an MCP server.

Anthropic developed MCP to give AI systems access to tools, data, and workflows through a common interface. Scientists can then link a large language model of their choice to the server, query the agent in everyday language, or run new experiments using their own data while borrowing the paper’s methods.

In other words, Paper2Agent is like a translator between a human-written paper and a chatbot.

That might sound redundant. After all, it’s already possible to upload a paper to ChatGPT, Claude, or another chatbot and ask questions. The difference is in the training: A chatbot can summarize a paper’s results, but it doesn’t have a deeper understanding of how those results came to be or whether the underlying analysis holds up.

By trying to replicate results based on the paper, Paper2Agent’s AI gets a sort of hands-on experience, potentially making it less prone to hallucination. The agents can “provide much more in-depth insights to the readers,” Zou told Nature.

In one experiment, Paper2Agent created an agent based on a recent paper describing AlphaGenome, an AI model that predicts how changes in DNA letters affect their function. It took under an hour without human supervision.

When asked genetics questions requiring AlphaGenome analysis, the agent answered with near-perfect accuracy. It was also two to four times faster than Biomi and other “AI scientists” given access to the same paper, likely because it had a better grasp of AlphaGenome’s tools and capabilities.

The tool didn’t always work. When the team applied it to 110 papers spanning computational biology, machine learning, astrophysics, and other fields, it successfully converted 76 percent into working agents. But that failure is arguably a feature, not a bug. Papers that couldn’t be agentified often lacked sufficient code, data, or model files, or relied on broken scripts and unavailable datasets.

“Agentification can therefore serve as a practical diagnostic of computational reproducibility,” wrote Zou and study author Jiacheng Miao.

Digital Collaboration

Left alone, the agents began “talking” to each other.

In one test, Paper2Agent converted two studies on psoriasis into agents. Working with the AlphaGenome agent, the trio found a previously unknown genetic variant as a potential cause for the condition. An agent based on a paper about ADHD genetics similarly flagged a new mutation associated with increased risk. Another pair of agents identified a DNA letter variant linked to “bad” cholesterol, one that AlphaGenome had not pinpointed on its own.

These results showcase the power of collaboration in a world where papers aren’t isolated documents. “In the past, if there are two research groups that publish two different papers, those two research groups have to somehow find each other,” Zou said. Paper agents could make that matchmaking automatic, allowing findings from different studies to mesh—and produce new ideas—with far less human effort.

Paper2Agent is free to use, although installing and running it still needs some computer savvy scientists may not have. For trainees, it could become a shortcut that stunts their ability to critically evaluate a paper and spot flaws in the authors’ logic, instead taking an agent’s reply at face value.

Other questions remain. If agents become a new way of sharing scientific knowledge, who should be responsible for creating them? And can papers based primarily on wet-lab experiments, rather than code, also benefit?

Magdalena Skipper, editor-in-chief of Nature, which published the work, doesn’t expect conventional papers to disappear anytime soon. But she is optimistic that agentifying papers could alter the future of sharing scientific knowledge.

“There is a prospect that…there will be something genuinely influential that will change the way knowledge is disseminated, but importantly, the way that knowledge is interacted with,” she said.