Provably Correct
Scientific Discovery
We build AI-driven frameworks for mathematically certified scientific discovery, combining the scale, pattern recognition, and learned creativity of generative AI with the certainty of machine-checkable proof.
The Architecture of Verified Discovery
Generative models can survey broad scientific literature, identify patterns, and propose candidate ideas at high speed. Logic-based verification provides the complementary function by checking claims through explicit, machine-checkable derivations. In combination, these systems support a closed-loop process for science and engineering: generate hypotheses, verify results as they emerge, and progressively automate reliable discovery.
Freedom to Conjecture. Know with Certainty.
The framework rests on four components, each with a distinct role.
Generative Models: The Creative Partner
Generative models, including LLMs, can survey more scientific and technical knowledge than any individual or team, identify cross-domain patterns, and extrapolate to generate candidate hypotheses, mechanisms, and designs. Their outputs remain probabilistic, often opaque in rationale, and prone to consequential errors.
Logic-Based Verifiers: The Rigorous Prover
Logic-based verifiers, including proof assistants such as Lean 4, validate claims through explicit machine-checkable derivations. Accepted results therefore carry strong correctness guarantees. The tradeoff is cost: full formalization is complex and often intractable at practical research speed without automation.
A New Scientific Paradigm
Generative systems expand the search space; logic-based verifiers certify correctness. The primary goal is automating new discovery that is correct from the ground up; the same infrastructure verifies the broader scientific record, including AI-generated output on track to overwhelm institutional review.
Benchmarks & Evaluation
Progress requires disciplined evaluation tied to scientific workflows. Our benchmarks measure whether generated outputs can be formalized and verified, track failure modes by category, and quantify improvements in proof success, turnaround time, and verification throughput.
What We Are Building
We apply this framework in concrete scientific domains, turning candidate results into reusable, machine-verified foundations for future research and for the AI systems trained on it.
Meet the Team
A cross-disciplinary team spanning physics, mathematics, and computer science: the combination required to build systems where scientific domain knowledge, formal reasoning, and implementation all have to be correct at once.
Invest in Verifiable Science
Funding partners receive early access to our verified theorem libraries and results, direct collaboration with our research team, and the opportunity to help shape the direction of our work. If your organization believes that the next generation of scientific AI must produce results that are not just plausible but provably correct, we invite you to support this effort and be part of building that future.
We also welcome individual contributors, academic research groups, and industry partners who want to build with us — whether by extending our Lean libraries, co-developing benchmarks, or applying formal verification to new scientific domains.
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