📑 AI scientist produces increasingly better papers – and an AI system can review them as well as humans

• The quality of scientific papers written by the AI system The AI Scientist increases predictably as the underlying AI models improve.
• An automated reviewer built by the same research group matches human reviewers' accuracy when evaluating scientific papers.
• The relationship between better models and better papers is statistically significant and follows a clear scaling law.

Better models produce better research

Last year, researchers at Sakana AI, the University of British Columbia, the Vector Institute and the University of Oxford showed that an AI system could produce a scientific paper that passed peer review at a workshop at the AI conference ICLR 2025. Now the full work is published in Nature, with new results showing how the system improves over time.

The researchers had The AI Scientist produce papers using a range of different AI models, from older to newer. The papers were then evaluated by an automated reviewer. The result shows a clear pattern: the newer and more capable the model used, the higher the quality of the produced papers. The relationship follows a scaling law and is statistically significant with a p-value below 0.00001.

The researchers also showed that more computational power per paper leads to higher quality. More computational nodes in the system's tree-based experiment search consistently produce higher scores. This means the system improves in two ways simultaneously: through better AI models and through more computational resources.

In practice, this means The AI Scientist does not need to be rebuilt to get better. The system automatically benefits from improvements in the underlying models.

Automated reviewer on par with humans

To measure paper quality at scale, the research group built an automated reviewer. It compiles five independent reviews and then makes a final decision in the role of "area chair," following the guidelines for the NeurIPS conference.

The reviewer was tested against thousands of real decisions from the ICLR conference. It achieved a balanced accuracy of 69 percent for papers published before the model's knowledge cutoff. That compares with 66 percent for human reviewers in the NeurIPS 2021 consistency experiment, where ten percent of all submitted papers were randomly sent to two independent review committees.

The reviewer's F1 score was 0.62. The agreement between human reviewers in the same experiment was 0.49. The automated reviewer was thus more consistent in its assessments than human reviewers were with each other.

Even for papers published after the knowledge cutoff – papers the model could not have seen during training – the balanced accuracy was 66 percent. This suggests that any data contamination had minimal effect on the results.

How the AI researcher works

The AI Scientist works in four phases. First, it generates research ideas and checks them against existing literature via Semantic Scholar. Then it conducts experiments through a parallelized tree-based search in four steps: preliminary investigation, hyperparameter tuning, main experiments and ablation studies. In the third phase, the system writes a complete scientific paper in LaTeX. Finally, the automated reviewer evaluates the paper's quality.

The system uses several AI models for different tasks. OpenAI's o3 handles idea generation, Anthropic's Claude Sonnet 4 writes code, OpenAI's GPT-4o analyzes images and graphs, and OpenAI's o4-mini handles the reviewing. The entire process takes between a few hours and over 15 hours depending on the complexity of the task.

All code is open and available on GitHub under the Apache License 2.0.

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