Deterministic AI is built on first principles. We publish our foundational research openly so engineers, enterprises, regulators and partners can independently evaluate the rigor, coherence, and long-term viability of the technology.
Foundational Research Paper
This paper introduces the Deterministic Computation Law (DCL), a formal mathematical framework establishing the necessary and sufficient structure for reproducible computation. Derived from three minimal axioms-input determinism, representation invariance, and replayable reasoning-the work proves that all reproducible computation must factor through canonicalization followed by deterministic reasoning. DCL provides an architecture-independent foundation for deterministic and auditable artificial intelligence across scientific, regulated, and safety-critical domains.
Published on Zenodo, CERN’s open research archive and assigned a permanent DOI.
Foundational Research Paper
This paper provides a theoretical foundation for several advanced computational and information-theoretic applications. By moving from a probabilistic view of information to a deterministic, representation-independent one, it offers unique solutions for verifying identity and conserving value across computations.
Published on Zenodo, CERN’s open research archive and assigned a permanent DOI.
Foundational Research Paper
This work establishes a formal requirement for determinism in artificial general intelligence by modeling AGI as a stateful dynamical system. It proves that reproducible reasoning, stable memory, identity continuity, and multi-agent synchronization necessarily require a deterministic cognitive core. The work provides a foundational theoretical constraint for building auditable, reliable, and scalable intelligent systems.
Published on Zenodo, CERN’s open research archive and assigned a permanent DOI.
Foundational & Cross-Domain Theory
This work formalizes the Deterministic Computation Law (DCL) as a mathematical framework for reproducible computation, bridging deterministic system behavior and probabilistic dynamics within a single model. Although developed using tools from theoretical physics, the results directly inform how deterministic AI systems, stable inference, and reproducible decision pipelines can be designed and analyzed. The paper establishes a law-level foundation for computation where identical canonical inputs provably lead to identical outcomes.
Published on Zenodo, CERN’s open research archive and assigned a permanent DOI.
Foundational Research Paper
This paper provides a foundational framework unifying Shannon information theory and Turing computation through deterministic, reproducible state representation. It clarifies the structural conditions required for probabilistic information to be reliably used within deterministic computation. The work establishes a theoretical basis for reproducibility, auditability, and stability in modern computing and AI systems.
Published on Zenodo, CERN’s open research archive and assigned a permanent DOI.