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. **Key Contributions:*** **DAIGS Category Foundation:** Formal specification of Deterministic Autonomous Infrastructure Governance Systems (DAIGS) as a scientific category.* **10-Layer Governance Architecture:** A complete, layered specification bridging probabilistic intent and sovereign physical action.* **Non-Negotiable Safety Invariants:** Seven hard-coded logic bounds (Confidence Threshold, Contradiction Resolution, Safety Override, Ambiguity, Temporal Consistency, Domain Boundary, Multi-Agent Consensus-by-Safety).* **Deterministic Explainability Engine:** Reproducible, rule-traced, dual-format (JSON + narrative) reasoning traces.* **Multi-Agent Arbitration:** Four-phase deterministic conflict resolution with safety primacy.* **LTC v1.0 Extension:** Ed25519-signed, SHA-256-chained certificate fabric providing cryptographic provenance.* **Real-World Viability:** Tested via drone-simulation yielding 100% unsafe action prevention at 4ms average latency. 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 absolute safety guarantees. This work forms the underlying governance layer for the Lume-Med, Lume-Fin, Lume-Ops, and Lume-X verticals.