OMNIA-1: A Deterministic Governance Shell for Safe LLM Interactions in High-Risk Domains

OMNIA-1 introduces a fully external, model-agnostic post-inference governance shell designed to enforce deterministic safety constraints on large language model (LLM) outputs in high-risk domains. By intercepting generated text and applying clinician-authored, observable boolean invariants, the framework mitigates stochastic semantic drift, looping, agency erosion, and dependency patterns—issues incompatible with strict human-oversight and robustness requirements under the EU AI Act (Articles 14 and 15). The modular architecture comprises a Context Classification Layer (CCL), Constraint Definition Module (CDM), ternary Validation Engine (ACCEPT / LIMIT / ESCALATE), and per-interaction cryptographic audit logging (SHA-256). Validation is based on 500,000 conservative simulated interactions, demonstrating a 64% reduction in unsafe states (95% CI: 61–67%), strong main effect in two-way ANCOVA [F(1,499995) = 1247.3, p < 0.001, partial η² = 0.38], and no significant increase in false-positive escalations (p = 0.34). OMNIA-1 enables auditable, compliant deployment of frontier LLMs in regulated sectors without modifying model weights or training data, providing a pragmatic deterministic containment layer for probabilistic systems.