Zeroth Research is a nonprofit organization dedicated to formal methods and artificial intelligence research, committed to building open infrastructure for the safety assurance of algorithmic systems. Their mission — "Making Intelligent Systems Safe, with Mathematical Certainty" — is pursued through a three-phase technical framework: mathematical modelling of deployment environments, formal certification of AI compliance during development, and continual monitoring for post-deployment safety assurance. The organization combines machine learning with automated reasoning to generate machine-checkable proof certificates that formally verify AI systems comply with safety and security specifications. Founded in mid-2025 and based in Birmingham, UK, Zeroth Research is closely affiliated with the University of Birmingham's School of Computer Science.
Zeroth Research is a nonprofit organization dedicated to formal methods and artificial intelligence research, committed to building open infrastructure for the safety assurance of algorithmic systems. Their mission — "Making Intelligent Systems Safe, with Mathematical Certainty" — is pursued through a three-phase technical framework: mathematical modelling of deployment environments, formal certification of AI compliance during development, and continual monitoring for post-deployment safety assurance. The organization combines machine learning with automated reasoning to generate machine-checkable proof certificates that formally verify AI systems comply with safety and security specifications. Founded in mid-2025 and based in Birmingham, UK, Zeroth Research is closely affiliated with the University of Birmingham's School of Computer Science.
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Theory of Change
Zeroth Research believes that empirical testing alone is insufficient to guarantee the safety of AI systems, and that formal mathematical proof is the only way to provide reliable assurance. By developing open infrastructure that combines machine learning with automated reasoning, they aim to enable AI systems to produce not just decisions but cryptographic proof certificates verifying compliance with formal safety specifications. This shifts AI safety from probabilistic/empirical assurance to mathematical certainty — analogous to safety guarantees in nuclear power or passenger aviation. Their open-infrastructure approach means these tools can be adopted broadly across safety-critical industries (avionics, automotive, medical devices, robotics), creating systemic impact on how AI systems are developed and deployed. By making safety verification accessible and rigorous, they reduce the risk of deployed AI systems causing harm through misalignment or adversarial exploitation.
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Details
- Last Updated
- Apr 7, 2026, 8:28 PM UTC
- Created
- Apr 7, 2026, 6:28 PM UTC