研究者が人間向けではなく AI 向けに論文を書くべきか議論

This May, 37 researchers from roughly two dozen top universities and tech companies published a paper on ArXiv, arguing that scientists should stop writing papers. Why? Because artificial intelligence needs a different format, and AI’s needs, they say, should be the priority.

“AI agents are becoming first-class participants in research workflows, not tools that assist humans but autonomous contributors that read, reproduce, and extend scientific work. That transition demands infrastructure built around agents from the start,” the authors write in the provocative article, titled “The Last Human-Written Paper.” The paper proposes a replacement, called an “Agent-Native Research Artifact” (ARA), that presents work in a format AI agents can use efficiently. (As an example, the paper itself is online in ARA form.)

image Jiachen Liu co-founded the Agent Native Research Lab in May. Jiachen Liu

The growth of AI tools in the research process is not without its critics, and scientists’ opinions about that shift are split. Some evidence shows AI-enabled research could boost individuals’ careers in a discipline but generate fewer new ideas and topics. Still, some biologists have come to see promise in AI as a “co-scientist.”

Lead author Jiachen Liu conducted work on the ARA proposal while pursuing her Ph.D. in computer science from the University of Michigan, which she was awarded in 2025. This May, she became a co-founder of the Agent Native Research Lab, an AI-for-science startup in Palo Alto, Calif. She spoke with IEEE Spectrum about the paper and the future of AI in scientific research.

Building infrastructure for an AI collaborator

How did you come to believe AI has become a collaborator for scientists rather than a mere tool?

Jiachen Liu: At the end of 2024 when the [Cursor] coding agent came out, I realized it had a great potential to replace me as a researcher. Yet I still needed to do a lot of harness on top of the AI [creating the infrastructure that guides the model and connects it to world]. It still needed a lot of manual work. I even wrote an article then to emphasize how the human was so important in the loop.

But AI has advanced since then. Already in 2026 there’s an almost complete undergrad level of knowledge inside the large language models. At some point soon, all the Ph.D.-level or professor-level knowledge will be inside those models. That’s the point where humans cannot provide more value. AIs will have to evolve further by themselves. So we’ll need an infrastructure that allows AI to safely and comfortably evolve. The ARA protocol is a first step to realize this.

What kind of response have you gotten to the paper?

Liu: I got diverse feedback, all of it positive. If they’re not positive, they probably don’t bother reaching out to you, right?

One type was from industry. They see this could make their research and knowledge systems more AI native. That could basically enable collaborations among the whole enterprise.

Another kind of feedback was from the academic researcher side. Everyone there sees that sharing research results has been a pain point for hundreds years, because any scientific breakthrough is a joint effort. It doesn’t come from individual brilliant scientists. It’s from a community effort, different people pushing in different directions.

The scientific paper was invented 350 years ago. Before that, scientists hid their research so that others would not scoop their ideas. After that, though, we get archives of work, we get peer review and conferences, and so on. Science starts progressing much faster. So that was a pivot point.

I think now is also a pivot point. Because now we have AI, we can unlock a lot of new opportunities. We’re inventing a new format to document research in a more efficient way, from first principles. Some nonprofit organizations are doing similar things, and there we could help each other.

You and your colleagues say the traditional scientific paper has two fundamental flaws from AI’s point of view. Can you explain what those are?

Liu: One is the “storytelling tax.” Once we write everything into a paper, 80 percent of the information about the work is lost. We only write down the last 20 percent. All the process, a lot of important decision making, the failures, the attempts that didn’t work out, they are all gone. In my work, I might spend a lot of time on fine tuning a small component, maybe just a parameter or several lines of code to make the system perform better. Yet none of that is shown in my final paper. Someone can read the paper, think the work is great, but they won’t learn what is actually the trick that makes it perform better on a certain workload. So many side branches get left out in creating the story of how the work was done.

Then, [even the information that does survive in the paper] is incomplete. That’s what we call the “engineering tax.” The paper itself is a lossy compression of the research process. So I cannot reproduce the work in the paper because either the language is too ambiguous or there are missing details of the implementation or experiments.

Why can’t we just train AI to adapt to humans—for instance, to interact with a researcher to get the information it needs?

Liu: Actually, a component of our ARA system is a “Live Research Manager,” which basically is a faithful AI observer of your entire research progress. So you, the researcher, don’t need to do anything about documenting research knowledge. Everything you do is automatically observed and documented in this protocol. So, if you want to publish it in today’s format, a paper in PDF, it’s easy to convert back to a polished story.

Checking for mistakes

Large language models make errors. They hallucinate. So how will humans be able to check all the work the AI does in this protocol?

Liu: A human being has limited bandwidth. So if you manually check all the code AIs generate, all the results, and all the analyses, that creates a bottleneck. [Instead the solution] is to use a formal system to objectively judge AI results. In other words, another layer of AI can easily supervise the process of the AI “scientists.”

What prevents hallucinations and mistakes in that AI?

Liu: I am working on a formal system using neurosymbolic techniques [that combine neural nets' use of unstructured data with symbolic AI's reliance on structures of logic and concepts]. That would guarantee that everything is rigorous. A language model alone, no matter how smart it is, has the chance to hallucinate because it’s a model based on probability, not logic. I want to make sure that I’m not using another language model to supervise the work done by an AI scientist.

It would make every research paper a formal system, so that every claim can be written by a mathematical formula, and proved by the system. That makes all the claims in the system self-consistent.

Getting rid of what you call the “narrative tax” means exposing mistakes, frustrations, or wrong turns to the world. What if researchers don’t want to do that?

Liu: I think that’s certainly a big concern. People don’t want to be perceived as dumb. But I see that preference as an opportunity for AI. For example, if an AI does 12 hours of work that doesn’t lead anywhere, the human who is steering the project can jump in and say, “oh, AI, you’re dumb. You’ve made ABC mistake!” Then that is totally fine with people. They’re showing they’re very smart to supervise AI’s work.

How long will humans have that steering role in AI research, though? Once you have AI supervising AI as you describe, will we reach a point where the AI doesn’t need human guidance?

Liu: Yes, I think that’s just where a lot of AI research in new labs is heading. I recently wrote an article called “The End of Human-in-the-Loop,” which describes why I’ve come to think there will be this singularity point. Once AI has “squeezed out” all the expert data from humans, it won’t need any more input from humanity. That is the time AIs will start just self-evolving by themselves. Right now, the human is the bottleneck. The AI is always waiting for input from humans. But so at some point, AI will just do more autonomous work.

If AI takes over so much scientific research, how will younger generations of human scientists get the experience and training they need to be able to steer future research, or even understand it?

Liu: A lot of people have this idea that with AI doing so much work, nobody cares about trying to make the junior engineers and scientists better. I don’t agree. I think people will grow better by learning from AI. People’s learning curve is very fast with AI. So actually, I think it will be fine. We’ll still have senior researchers, senior engineers. But they will have had totally different learning experience than [earlier generations].