Lume-V: A Deterministic Governance Layer for Nondeterministic AI Systems

Modern Artificial Intelligence systems — ranging from large language models to multi‑agent architectures and vision classifiers — are fundamentally nondeterministic, opaque, and prone to unpredictable failure modes. These characteristics make them structurally incompatible with safety‑critical environments such as robotics, autonomous vehicles, industrial automation, and real‑time operational control loops. In this paper, I introduce Lume‑V, a deterministic governance layer that validates, explains, certifies, and arbitrates AI decisions before they reach downstream systems. Rather than attempting to force determinism onto the probabilistic models themselves, Lume‑V acts as an immutable state machine wrapper. It enforces a strict set of seven non‑negotiable safety invariants, generates deterministic explainability traces, issues Ed25519‑signed trust certificates anchored to the Lume Trust Certificate (LTC v1.0) architecture, and performs multi‑agent consensus‑by‑safety arbitration. I define a 10‑layer architectural specification for Lume‑V and demonstrate its effectiveness through a drone control simulation where injected timing and logic faults are safely intercepted, yielding 24 validated decisions, 18 approvals, 6 deterministic overrides, and zero unsafe actuator commands at an average latency of 4ms. Lume‑V establishes the foundational mechanism for Deterministic Autonomous Infrastructure Governance Systems (DAIGS), providing a practical, rigorous, and verifiable bridge between nondeterministic AI and real‑world systems requiring deterministic safety guarantees.