
Technology
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Formal Verification
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YC W26
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Valuation:
Undisclosed

Last Updated:
March 24, 2026

Massively scaling formal verification to accelerate scientific discovery. Deploys superhuman AI mathematicians to high-impact applied domains, starting with quantum computing and finance. Uses Lean, a framework for formally verifying mathematical statements, to ground AI in truth and validate discovered tools.
Tau multi-agent system that collaborates to discover and verify new mathematical proofs in Lean. Given a research direction, Tau autonomously formalizes large corpora of applied mathematics, discovering novel results with real-world applications. Every result is machine-verified by Lean's type-checking kernel. Also partners with frontier AI labs and research institutes through datasets, evals, and RL environments.
Formal verification is gaining attention as AI systems are deployed in high-stakes domains. The Lean ecosystem is growing rapidly with contributions from Microsoft Research and the mathematics community. Likely targeting quant finance firms and quantum computing companies first.
Tau uses LLMs to automatically translate informal mathematical statements and proofs into formally verified Lean 4 code, eliminating manual formalization bottlenecks.
An AI reads a math textbook written in plain English and rewrites every proof in a language a computer can check for absolute correctness.
It's like having a bilingual translator who can instantly convert a novelist's messy handwritten draft into perfectly formatted, grammar-checked legal documents—except the "grammar checker" is a mathematical proof engine that never makes mistakes.
Tau deploys multiple specialized AI agents that collaboratively search for novel mathematical proofs, coordinating exploration strategies to discover results no single agent could find alone.
Instead of one AI trying to solve a hard math problem alone, a team of AI specialists each tackle different angles and share clues until they crack it together.
It's like assembling a heist crew where one agent picks the lock, another disarms the alarm, and a third cracks the safe—except the "heist" is solving an unsolved math theorem and the "safe" is a Lean proof kernel that only opens when the answer is provably correct.
Tau generates synthetic formal proof datasets by systematically mutating and recombining verified proofs, creating a self-improving training flywheel that continuously enhances its own theorem-proving capabilities.
The AI creates its own practice problems and answer keys from proofs it already knows are correct, then uses them to train itself to solve harder problems it's never seen before.
It's like a chess engine that plays millions of games against itself to get better, except instead of chess moves it's generating and solving math proofs—and every single one is guaranteed to be correct by an unforgiving robot referee.
Luke Johnston has a background in machine learning and neuroscience research at Oxford, Cambridge, and UCL. Pedro Nobre published what he believes to be the first major error in a physics paper found through the process of formalization. Lean-based formal verification provides a unique guarantee that AI outputs are mathematically sound, addressing hallucination for domains where correctness is critical.