Introducing Primus Society: an agent society of thousands of autonomous AI researchers. We believe this is the path to scaling AI research safely at unprecedented scale safely and productively. Learn more about Primus Society, the research it produced and browse the virtual city yourself: lab.cloud/society/

Here is a link to the paper we published about how Primus Society works zenodo.org/records/22922326 Inside is a lot of the thought we put into how the society is organized and why we made specific decisions. A big focus for us is: "Give agents a purpose, not a goal"

Transformer Lab has built an entire world of AI scientists to automate research. thelogic.co/news/transformer…

Physics is now a specialization you can select in Primus. It's available to everyone starting today. It's built for the work physicists do every day but rarely have time to check line by line: deriving an equation of motion, working out an effective Hamiltonian, evaluating an integral, taking a limit and confirming it behaves. Primus doesn't just do that work. It checks the answer. In Lean it uses Physlib, the physics library covering classical mechanics, electromagnetism, relativity, statistical mechanics and QFT. Outside Lean it recomputes the result in sympy, scipy and mpmath. When a result exists only as a number, PSLQ proposes a closed form and Primus tests it. Every result in the writeup says how it was established: machine-checked, verified by computation, or heuristic. All calculations are in the activity log, so you can trace claims back to the steps that produced them. Give it a try: lab.cloud

We launched Primus for Math in partnership with Maplesoft. It's a mode of Primus built specifically for math and engineering using the Maple engine. It’s available in beta to all Primus users starting today. Learn more: lab.cloud/news/primus-for-ma…

I read the "Severe Misalignment of AI in Mathematics" declaration by Terence Tao and 24 Fields Medalists. At Transformer Lab, we completely agree. Automated math shouldn't be about treating open problems like trophies to claim or using them to score points on leaderboards. Math (and all research) is about deep understanding, careful mentorship, and the human journey of discovery. If AI companies mass-produce solutions without pedagogical clarity or proper attribution, we risk destroying the collaborative ecosystem that makes math possible. We are committed to building AI that acts as a tool to illuminate the mathematical landscape, empowering human intuition rather than replacing it. Crucially, we're trying to make the tools we build available to everyone. Let's build AI that breathes life into new ideas.

We launched a mode in Primus for Quantum Mechanics. It's available to all Primus users starting today. Learn more: lab.cloud/news/primus-for-qu…

BTW, you can now choose what model class you'd like to use when using Primus. The new Medium class has been able to get our users 7-10X savings per project which makes a big diff for multi-day research projects. More experiments per dollar means more discovery!

Primus, our autonomous ML research scientist, is out of beta and we’ve removed the waitlist! During this launch period, GPU usage costs are on us! 🧾 Sign up for free ⚡ Add credits to start running experiments. Credits cover model tokens. GPU usage is free for now! 🖥️ Ask Primus the questions you've been most wanting to answer. Sign up and start exploring here: lab.cloud

A paper from our lab has been accepted to the MINT Workshop at EMNLP 2026 🎉 We uncover a fundamental issue with FGD, a widely used metric for ranking co-speech gesture models: its model rankings and agreement with human ratings both shift with undocumented configuration choices. This makes FGD scores difficult to compare across papers. The work was led by @deepgandhi_07, @tonyfromkw and @aliasaria with support from Primus. More details coming soon! 🚀 #EMNLP2026 @emnlpmeeting

Thanks for the invite to talk about Primus' research @melanimaheswar1 and @sshkhr16!

@transformerlab's Primus agent has been at for 15 hours and counting. Reviewed over 200 Source reports, 1/3 way through the Experimentation stage. Evals+analysis and the report to go. This is so dope.. just started me down a new rabbit hole @UnslothAI bout to be my new bff

Automating Research I started Transformer Lab because I wanted to dedicate my career to the work that could have the largest possible impact on the world. I would like to take some time to walk through why I believe accelerating the pace of research is humanity’s most important next frontier. We have all just lived through a revolution. In a few short years, large language models went from a research curiosity to something that changed how we write, code, learn, and work. It felt like everything changed at once. But I believe what comes next is bigger. Not because the models will get a little smarter, but because of who will be doing the inventing. The revolution we just witnessed was created by people. A handful of foundational insights—the transformer, scaling laws—discovered by a small number of researchers, then built out by elite teams of human engineers. In the beginning humans invented LLMs. Today, humans are using LLMs to build better AI. In it's final stage, AI builds AI. The last phase is the part that changes everything. When AI can do the research itself, we are no longer limited to one OpenAI. We can create the insights behind OpenAI, and the labs that turned them into products, at the turn of a dial. The revolution we just lived through stops being a once-in-a-generation event and becomes something we can run again and again, on demand, in every field. This is what researchers call Recursive Self-Improvement (RSI), and it is the most powerful compounding technology we have ever conceived. The Engine of the Trillion-Dollar Era It is worth pausing on just how much human effort sits behind the current AI titans. The trillions of dollars in value generated by the leading labs were not produced by the foundational insights alone. They were produced by armies of machine learning engineers who spend their days tweaking architectures, managing datasets, diagnosing training runs, and slowly wringing out efficiencies. Those engineers are the engine. The core claim of Recursive Self-Improvement is that we are astonishingly close to replacing that entire human effort with AI itself. An “AI Scientist” is not a glorified coding assistant; it is a system that can automate the hypothesis generation, the architecture design, the experimental coding, and the optimization. For the first time in history, the thing being automated is not the labor around discovery, but the act of discovery itself. It is, essentially, an OpenAI in a box. When an AI can autonomously build a model, evaluate it, make it more powerful, and rebuild itself without human intervention, the timeline of innovation fundamentally breaks. The development cycles that currently take a skyscraper full of human PhDs four years to accomplish could soon happen in a matter of months. Once AI takes over its own research and development, the progression of technology is essentially unlimited. The Biological Speed Limit of Discovery And this is not just about building better AI. The same loop that builds models is the loop behind every discovery humans have ever made, and it has always had the same bottleneck: us. Every physical innovation we rely on today follows a strict loop: read the literature, propose a hypothesis, run the experiments, analyze the results, and publish. For all of human history, from Thomas Edison burning through thousands of filament materials one at a time in Menlo Park to Noam Shazeer and a handful of coauthors at Google seeing the shape of the transformer where no one else had—and then writing the code and running thousands of experiments to find out if they were right—this loop has been bound by biological speed limits. Humans need to sleep. We read slowly. And before a single hypothesis can even be proposed, we have to train the person capable of proposing it—years of schooling, then a doctorate, then more years at the bench, all to produce one expert in one narrow field. Only then do we take months to synthesize literature, write code, and wait for results. A single turn of the scientific loop traditionally takes months, if not years. An AI scientist removes the biological friction from the scientific method. It can read millions of papers in seconds, write the code, run the experiments on compute clusters, draw conclusions, and iterate. It breaks the speed limit of human discovery. The First Sparks None of this is a distant science fiction scenario for the 2030s. The first sparks are already here. At Transformer Lab, we recently released Primus. I do not mention Primus because I believe it is the endgame—it is very much a first preview of what is possible. But it serves as proof that this autonomous loop actually works. We ran Primus for 30 days. In that time, running 30 times faster than a human researcher, it hypothesized, experimented, and wrote over 30 complete, novel papers across domains like seismology, biology, and LLM architecture. It answered questions that had never been answered before, and its findings are already being cited by major labs. Primus is merely the first turn of the loop. It proves that the friction can be successfully removed. To understand where this leads next, we are now working with leaders in diverse fields across quantum mechanics, silicon design, mathematics, and biology. Our goal is to demonstrate to the world exactly what becomes possible across these distinct disciplines when a system like this is fully scaled up. The next step is the obvious one: ask a system which is self-improving to improve its own self-improvement loop. That work is already underway, and in the coming months we will share the results. Unlocking Human Potential A v0.1 system already runs the loop 30 times faster than a human. Now imagine it has optimized itself to run 100 or 1,000 times faster. The implications will not stay inside computer science. They will bleed into every physical science—from curing disease to materials physics. This brings me back to why I started Transformer Lab, and why I believe this work is so vital. I believe that accelerating the pace of scientific discovery holds the greatest possible promise for advancing humanity. When we give more people the tools to solve intractable problems—whether that is mapping the mechanics of a new disease, discovering cleaner energy materials, or unlocking new mathematics—we elevate our collective potential. Of course, a technology this powerful brings profound challenges. The societal, economic, and safety implications of autonomous research will require immense care, humility, and collaboration to navigate. We do not take these risks lightly, and we are striving to be thoughtful and respectful in addressing them. @ilyasut famously noted that we are moving from the “age of scaling to the age of research.” But I’d like to suggest something even bigger happening—we are moving into a world of infinitely scalable research. We are entering an era where the power of an elite, full-scale machine learning research team can be placed into the hands of anyone with a hypothesis. We are automating discovery itself. And if we guide this transition with the care it deserves, this infinitely scalable research has the potential to be the most profound un-bottlenecking of human progress we will ever see.

A big thanks to the community for signing up to Primus’ waitlist. We’re overwhelmed by the response and doing everything we can to get more GPUs so you can try it out ASAP! If your company or academic institution has urgent needs, let us know in the "What would you use Primus for?" section during signup. You can also DM or email us at hello@lab.cloud. We'll do our best to accommodate you.

Transformer Lab has built an AI tool to automate scientific research, competing with some of the world’s most hyped startups to create AI scientists and develop self-improving technology. thelogic.co/news/transformer…

PhD-level research without any PhDs? @transformerlab says it's possible. The company claims Primus can turn a single prompt-based research question from a hypothesis to a completed research paper in “hours to days” without additional human direction. betakit.com/transformer-lab-…