Announcing Lanyon AI

Today, Lanyon AI is coming out of stealth. We’ll be progressively revealing more information (and showing some cool demonstrations of our capabilities!) in the coming days and weeks, and we’re so excited to start sharing more details about what we’ve built so far, and where we’re going next. As you will soon see, our research and development agenda is both radical and ambitious, spanning the frontiers of mathematics, physics, artificial intelligence, and scientific computing. But for now, I want to give you a brief preview of what’s to come.

Formally Verified AI for the Future of Science

It’s 2026. Code is now abundant, but correctness remains scarce. For science, engineering, and other mission-critical tasks, correctness is non-optional and mathematical precision is paramount. As artificial general intelligence matures, and continues to play an increasingly all-encompassing role in the computational future of our species, how can we trust that the scientific and technical infrastructure that we are erecting is built upon anything other than sand (even if that sand, miraculously, can now think)?

For some time now, the answer to this question has seemed obvious: autoformalization. If LLMs can write code, then they can also write proofs. Why not just have the LLM write a piece of code, and then either simultaneously or sequentially write a formal proof of correctness for that code in a proof assistant language such as Lean, or Rocq, or Agda?

It’s a clever approach, and it certainly has had some success in mathematical domains, but it suffers from a fundamental risk of misformalization: where the implementation and the proofs don’t actually match. What happens when the implementation of an algorithm contains a plus sign, but the corresponding proof of its correctness contains a minus sign? These are the kinds of errors that LLMs can introduce all too easily, yet they immediately render any apparent “formalization” worthless: the proof might well type-check, but the implementation can still be wrong.

Instead, what we’re pioneering here at Lanyon AI is a fundamentally neurosymbolic approach: using LLMs to do what they’re good at – being creative and proposing interesting ideas – while exploiting symbolic theorem-proving methods for doing what they’re good at – being unfailingly reliable and predictable.

In mere seconds, our proprietary AI agent, simply called Lanyon, can conjure simulations of complex physical systems, prove sophisticated mathematical theorems, and invent new state-of-the-art algorithms, with only a tiny fraction of the token and compute cost of frontier AI models (even with the best available coding and autoformalization harnesses). We will be releasing public benchmark scores attesting to this fact in the coming weeks. Even more remarkably, unlike any other frontier AI model currently available, any code or data generated by Lanyon is provably correct by construction: it is mathematically impossible for Lanyon ever to make a mistake¹.

Correctness by Construction

It sounds like a miracle, but it’s actually an inevitable consequence of the way that Lanyon is designed. The core idea is very simple. In 2026, why are agents still reasoning and coding in imperfect human languages? Why not have agents read, write, and reason in a single, unified, formal language, from which implementations and proofs of correctness can be generated simultaneously?

Lanyon is much more than an LLM harness or a coding agent wrapper. It is a radically new kind of neurosymbolic architecture, where the LLM proposes a formal specification in our own proprietary domain-specific language (DSL), and then purely symbolic algorithms expand that specification into code and proofs simultaneously. If the specification cannot be rigorously proven to be correct, the code simply doesn’t generate. So Lanyon is structurally incapable of committing errors or misformalizations.

Lanyon’s formal specification language is mathematically, physically, and scientifically aware, and both encodes and distills many decades of collective research expertise from our founding team. But it also has another key advantage: because it is so highly condensed and domain-specific, Lanyon is not only 100% reliable, but also blisteringly fast and ultra-low-cost compared to existing frontier models. Lanyon reasons at the level of tens of lines of formal DSL, but these lines are then automatically and instantaneously expanded into tens of thousands of lines of code and proof by our symbolic algorithms. At top speed, Lanyon can write thousands of lines of type-correct mathematical proof every second, and as we continue to extend and refine our specification language, it’s only getting faster and smarter with each passing day.

Lanyon’s proprietary DSL for math and physics, our symbolic theorem-proving algorithms, our automatic code-generation functionality, and even our low-level agent design, are all the byproducts of decades of research from our founding team, working at the frontiers of applied mathematics, computational physics, automated theorem-proving, and scientific AI. We see Lanyon as our unique opportunity to democratize the deep knowledge and expertise that we have accumulated along the way, and we are so incredibly excited to see what cool systems people are now able to simulate, what hard problems people are now able to solve, and what world-changing ideas people are now able to pursue, thanks to the technology that we’ve built and are continuing to extend.

Hard Problems, Exciting Opportunities

Even aside from AI, the coming decade is going to force us to confront civilization-defining engineering challenges and scientific opportunities: deep space exploration, clean and renewable energy, critical infrastructure development. In recent decades, scientific computing – and with it, the ability to run trustworthy and accurate simulations of the physical world – has been instrumental to our progress on these and related problems. AI holds the promise of accelerating this progress into the stratosphere, but now places an even greater burden on trust, accuracy, and verification. At Lanyon AI, we are trying to build the future of scientific and technical computing on a formally verified and mathematically rigorous foundation.

Our initial target areas will be critical industries: aerospace engineering, space and atmospheric propulsion, nuclear energy. But over the long term, we see Lanyon as something much bigger: the formally verified substrate that connects AI to the physical world, and that breaks down the barriers between what is physical, what is computational, and what is intelligent.

We can’t wait to show you more.