Kernel 10.X: A Clinician-Governed Deterministic Safety Infrastructure for AI-Mediated Interactions

Large Language Models (LLMs) rely on probabilistic inference, introducing variability that limits their suitability for regulated, safety-critical, or vulnerability-sensitive contexts. Kernel 10.X is a deterministic safety infrastructure designed to constrain AI-mediated interactions within a formally verifiable operational envelope. The system does not perform diagnosis, assessment, or treatment. Instead, it enforces clinician-authored safety invariants derived from established clinical practice, cognitive science, and descriptive DSM-5 anchors translated into observable interaction variables.Kernel 10.X operates as an external governance layer, independent of model weights and training data, ensuring predictable enforcement regardless of underlying model behavior. Shadow-environment testing on 750,000 simulated and mirrored interaction streams demonstrates a significant reduction in unsafe interaction states without increasing false-positive escalation rates. The framework provides deterministic enforcement, cryptographic auditability, and explicit human-oversight pathways, aligning with the robustness, transparency, and oversight requirements of the EU AI Act.