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Excited to share The AI Scientist! We use LLMs to autonomously come up with research ideas, implement them, do literature search, write them up, and review them -- producing full-length papers on AI without human intervention.
Co-led with @cong_ml and @RobertTLange
Introducing The AI Scientist: The world’s first AI system for automating scientific research and open-ended discovery!
sakana.ai/ai-scientist/
From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery.
Here are 4 example Machine Learning research papers generated by The AI Scientist.
We published our report, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, and open-sourced our project!
Paper: arxiv.org/abs/2408.06292
GitHub: github.com/SakanaAI/AI-Scien…
Our system leverages LLMs to propose and implement new research directions. Here, we first apply The AI Scientist to conduct Machine Learning research. Crucially, our system is capable of executing the entire ML research lifecycle: from inventing research ideas and experiments, writing code, to executing experiments on GPUs and gathering results. It can also write an entire scientific paper, explaining, visualizing and contextualizing the results.
Furthermore, while an LLM author writes entire research papers, another LLM reviewer critiques resulting manuscripts to provide feedback to improve the work, and also to select the most promising ideas to further develop in the next iteration cycle, leading to continual, open-ended discoveries, thus emulating the human scientific community. As a proof of concept, our system produced papers with novel contributions in ML research domains such language modeling, Diffusion and Grokking.
We (@_chris_lu_, @RobertTLange, @hardmaru) proudly collaborated with the @UniOfOxford (@j_foerst, @FLAIR_Ox) and @UBC (@cong_ml, @jeffclune) on this exciting project.
Chris Lu retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Chris Lu retweeted
Announcing @xdofai: We’ve raised $70 million to build the core robotic infrastructure ecosystem for robot foundation models.
My cofounders Fred (@YideShentu), Nemo (@itsnemojin) and I have been pursuing the dream of general purpose robots for our entire lives. After work at Covariant, Meta and Tesla, it became clear to us that general purpose robots are coming, and we are building XDOF to help make them a reality.
For the last two years, we’ve been working behind the scenes to support major labs and companies deploying robots. In us, they have a partner with full-stack expertise, from hardware to operations to policy training.
As our first public contribution to the space, we are open-sourcing ABC-130K, the largest open source teleoperation dataset, in collaboration with our partners from UC Berkeley, Carnegie Mellon, MIT and Amazon FAR.
Thank you to our customers, partners, collaborators and investors for your trust and conviction in us. Together, we can accelerate the future of robotics!
Chris Lu retweeted
We’re excited to introduce Inherent, a lab designed from scratch to build AI agents that discover new knowledge.
The coming era of machine-driven scientific inquiry demands a new kind of research institution and a new kind of AI.
To achieve our mission, we live within the experiment, recursively self-improving the entire research organisation. We investigate questions including:
- What does ‘AI taste’ look like in the sciences, and how can we build an institution that embraces this new aesthetic of discovery?
- What new kinds of human-machine teaming will make the most of AI that can truly innovate?
- How can we build recursive self-improvement at the collective level that continually increases human agency over outcomes?
We have just closed a $50m seed round led by @IndexVentures and @radicalvcfund, with participation from other outstanding investors including NVentures (@nvidia's venture capital arm), @buildexante, Metaplanet, Macroscopic, @MythosVentures, Charlie Songhurst, @chalfs, @jluan, @dwarkesh_sp, @Thom_Wolf, @j_foerst and @maxjaderberg. We are advised by @matthewclifford.
Inherent is a Public Benefit Corporation headquartered in London.
Chris Lu retweeted
I've recently got in on the act of getting AI to solve open problems in mathematics. More precisely, I gave some questions asked by Melvyn Nathanson to ChatGPT 5.5 Pro, to which I have been given access, and it answered them. 🧵
Chris Lu retweeted
Arena Trends: Text-to-Image, Jan 2026 – Apr 2026
For most of the year, @GoogleDeepMind and @OpenAI traded the top spot within a tight margin - GPT-Image vs. Nano Banana - with the rest of the field clustered below 1,200.
Today, GPT-Image-2 breaks away with a score of 1,512, 242 points ahead of #2 Google.
The frontier continues to move.
Exciting news - GPT-Image-2 by @OpenAI has claimed the #1 spot across all Image Arena leaderboards!
A clean sweep with a record-breaking +242 point lead in Text-to-Image - the largest gap we’ve seen to date.
- #1 Text-to-Image (1512), +242 over #2 (Nano-banana-2 with web-search aka gemini-3.1-flash-image)
- #1 Single-Image Edit (1513), +125 over #2 (Nano-banana-pro aka gemini-3-pro-image)
- #1 Multi-Image Edit (1464), +90 over #2 (Nano-banana-2)
No model has dominated Image Arena with margins this wide.
Huge congratulations to @OpenAI on this major breakthrough in image generation! More performance breakdowns by category in the thread below.
Chris Lu retweeted
Introducing ChatGPT Images 2.0
A state-of-the-art image model that can take on complex visual tasks and produce precise, immediately usable visuals, with sharper editing, richer layouts, and thinking-level intelligence.
Video made with ChatGPT Images
Chris Lu retweeted
Optimize materials yourself! 🔥
No cloning, no install — just open our interactive Google Colab demo and start optimizing crystals for formation energy or band gap in < 2 minutes.
→ Try it here: colab.research.google.com/dr…
→ Or grab the repo + open weights: github.com/znowu/CliqueFlowm… huggingface.co/iamkuba/Cliqu…
Chris Lu retweeted
Still remember the experiment grind over New Year's break--really great to see this out in Nature today!
AI automation of AI research is heating up fast, and I'm excited to see what becomes possible as models keep improving (see the figure below!)
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
One of the most exciting findings in our @Nature paper is the discovery of a clear scaling law of AI science. By using our Automated Reviewer to grade papers generated by different foundation models, we observed that as the underlying models improve, the quality of the generated scientific papers increases correspondingly.
Crucially, we expect this scaling to work in two ways: through more capable foundation models, and by scaling up compute during inference. This implies that as compute costs decrease and model capabilities continue to increase, future versions of AI Scientists will be substantially more capable.
You can see this trend in the attached chart and read the full open access paper (PDF) here: nature.com/articles/s41586-0…
Chris Lu retweeted
I’m incredibly proud of The AI Scientist team for this milestone publication in @Nature. We started this project to explore if foundation models could execute the entire research lifecycle. Seeing this work validated at this level is a special moment. I truly believe AI will forever change the landscape of how scientific discoveries and scientific progress are made.
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
It is great to see this collaboration across @FLAIR_Ox @SakanaAILabs and @UBC recognised for what it is: One of the first signs of life of a new paradigm that is now going at full speed and will change the world. Congratulations to the entire team and special shout out to my (now former!) student @_chris_lu_ for whom this is the crowning achievement of an amazing DPhil that went from multi-agent learning and opponent shaping to meta-learning via "RL at the Hyperscale", LLM as search operators over code, all the way to the end-to-end AI scientist.
There are so many debates about whether a Phd is useful in the age of "scale is all you need", so this is a refreshing datapoint.
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
I am really excited to share that our work on The AI Scientist has been published in Nature
Automated Scientific Discovery has been something I only dreamt about at the start of my PhD. Today, we are making big leaps into a world in which autonomous agents support human researchers in tackling some of the most fundamental problems.
In August 2024, The AI Scientist-v1 showed first sparks of LLM agents becoming capable of conducting research end-to-end. While the generated artifacts were still far from perfect, it was clear that automated discovery was about to change. We scaled the system and improved all ingredients of the pipeline. In April 2025, The AI Scientist-v2 had become capable of producing a paper that could pass the human peer review of an ICLR workshop.
This is only the beginning. Systems like AlphaEvolve, ShinkaEvolve, AIDE, and Autoresearch will continue to shape the future of how research is conducted. Our METR-style scaling results indicate that model improvements have direct downstream impacts. Still, there are many challenges. Both technical and societal. I have a strong belief that we, as a collective, will find the answers and adapt.
This has been an enormous amount of work by an outstanding set of human researchers @_chris_lu_ @cong_ml @_yutaroyamada @shengranhu @j_foerst @jeffclune @hardmaru @SakanaAILabs with many long nights of work. I am super grateful for the entire ride, learnings and the future to come. Thank you to everyone!
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature!!✨
Today in Nature we share a comprehensive technical summary of our work on The AI Scientist, including new scaling law results showing how it improves with more compute and more intelligent foundation models.
The AI Scientist autonomously creates its own research ideas, codes up and conducts experiments to test those ideas, creates figures to visualize the results, writes an entire scientific manuscript summarizing what it has discovered, and conducts its own “peer” review of the resulting paper. One of its papers–entirely AI generated–passed peer review at a top-tier AI conference workshop, a historic milestone marking the dawn of a new era of AI-accelerated scientific discovery. 🔬🧪✨🧬💡🔭
Paper nature.com/articles/s41586-0…
Blog sakana.ai/ai-scientist-natur…
Work done in collaboration with a great team from Sakana, Oxford, and my lab at UBC. Thanks and congratulations everyone!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru
🤖 Made with AI
Chris Lu retweeted
When we released The AI Scientist, it felt like the far future. Fast forward to today, and the automation of research is on everyone's mind.
Thrilled that our foundational work has been published in @Nature!
Please check out the paper along with some fun new scaling laws! 😃
🤖 Made with AI
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
The AI Scientist V1 was completed months before o1-preview and reasoning models were released. The models have clearly gotten much more capable since then. Very excited for where things are headed for AI and automated research!
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature
Nature: nature.com/articles/s41586-0…
Blog: sakana.ai/ai-scientist-natur…
When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle.
From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible.
Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process.
Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature!
This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement.
Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable.
Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science.
This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team!
@_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
Chris Lu retweeted
It's getting harder and harder to get signal from benchmark numbers. Rather than averages, I except in the (near) future we will also care about "argmax": what's the BEST output a model can deliver? After all, we don't need to solve PvsNP 10 out of 10 times, once is enough 😅. So with that in mind let me tell you a bit more about THE MOST IMPRESSIVE LLM OUTPUT I have ever seen.
Chris Lu retweeted
✨ GPT-5 crossed a major threshold: over the last two months, we’ve heard repeated examples of scientists successfully directing GPT-5 to do novel research in math, physics, biology, CS, and more.
If you have an example to share, please reply below! Prizes for the best ones 🧵👇
Chris Lu retweeted
Yet more evidence that a pretty major shift is happening, this time by Scott Aaronson
scottaaronson.blog/?p=9183&f…
