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Research papers can be hard to use.
You may need to read the paper, find the right method, search through supplementary files, install code, find the data, and figure out whether the method can work for your own question.
Even when everything is publicly available, putting it all together can take a lot of time.
The Nature paper, “Reimagining research papers as interactive and reliable AI agents,” introduces Paper2Agent, a system that turns research papers into interactive AI agents. The idea is that a research paper is more than just a PDF.
source: https://www.nature.com/
What if a research paper could do more than explain? That could change how we use scientific research. So, Paper2Agent asks a simple question: what if a research paper could talk back?
These agents can explain methods, run analyses, use methods on new data, and work with agents based on other papers. It also includes code, data, supplementary files, workflows, and examples.
Paper2Agent brings these pieces together and turns them into an interactive AI agent. You can think of it as a virtual research assistant built around a paper.
A researcher could ask:
-What does this method do?
-How was this result produced?
-Can you run the analysis?
-Can I use this method on my data?
-Where does this tool come from?
The system can also connect its tools back to the original code, making it easier to see where the results come from.
This is more than uploading a PDF to ChatGPT
A chatbot can read a paper and explain it, but Paper2Agent tries to make parts of the actual research workflow available too. That means a researcher could move from reading about a method to trying it without rebuilding the entire workflow from scratch.
source: https://www.nature.com/
The researchers tested it on real scientific work. The authors applied Paper2Agent to several computational biology projects, including AlphaGenome, Scanpy, and TISSUE.
The agents were used for tasks such as genomic analysis, single-cell analysis, and spatial transcriptomics. The researchers also tested whether agents based on different papers could work together.
In one example, several agents combined different types of genetic evidence to help prioritise a possible causal gene linked to psoriasis.
The interesting part is the connection between the papers. A method from one paper could interact with data and findings from another.
Why this may matter
A lot of scientific knowledge is difficult to reuse. You may understand the idea in a paper but still struggle to run the code, find the right data, or adapt the method to your own problem. Paper2Agent could reduce some of that work.
It could make research methods easier to explore and reuse, especially when the technical details are difficult to reconstruct. It also suggests a new way of thinking about the research paper.
A paper could become more than a record of completed research. It could become an interactive tool for exploring and extending that research.
But, there is still a big question: can we trust it?
Making a research workflow executable does not automatically make the results reliable. An agent could use the wrong data, misunderstand a parameter, generate incorrect code, or apply a method in a situation where its assumptions do not hold.
Paper2Agent includes safeguards such as tests, comparisons with reference results, and links back to the original code. These can help. But researchers still need to check the results and understand the assumptions behind the method.
That raises some important questions as well:
Who checks that the AI agent represents the original paper correctly?
Who is responsible when a method is used in a new context?
How do we know which parts of an answer come directly from the research and which come from the AI?
These questions will become more important as research agents become more common.
What could come next?
The authors imagine a future where thousands or millions of paper agents can interact with each other. Imagine finding a paper, asking its agent how the method works, running it on your data, and then connecting it to another paper's agent. That could make the scientific literature much more useful and interactive. It could also add a new layer between researchers and the evidence.
Paper2Agent points toward a future where papers do not just tell us what researchers did. They may also help us do it.
But another important question is whether they can also help us understand the science well enough to know when we should.
One question to think about:
Is this making the evidence easier to examine, or simply making the workflow easier to run?
Sources
Miao, J., Davis, J. R., Zhang, Y., Pritchard, J. K., & Zou, J. (2025). Paper2agent: Reimagining research papers as interactive and reliable ai agents. arXiv preprint arXiv:2509.06917.
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Maryam