Lume‑Ind introduces a deterministic governance substrate for nondeterministic AI systems in cyber‑physical industrial environments — manufacturing, robotics, logistics, fleet operations, warehousing, supply chain management, predictive maintenance, energy systems, construction, and industrial IoT. It unifies invariant‑based validation across five industrial risk categories (safety, quality, throughput, logistics, temporal), deterministic arbitration with safety‑dominant ordering, cryptographically verifiable LTC‑Ind trust certificates with Ed25519 signing and SHA‑256 hash chains, deterministic explainability at operator, supervisor, and regulator levels, and a real‑time Ind Runtime that executes the full governance cycle in deterministic time, order, state, and memory. I define a 63‑block architectural specification covering the complete governance stack from invariant hierarchy through certificate serialization, multi‑agent coordination, fail‑safe escalation, deterministic logging and telemetry, configuration governance, deployment and upgrade models, safety envelope enforcement, facility identity and key management, multi‑facility governance, human‑in‑the‑loop integration, audit and regulator interfaces, genesis bootstrapping, simulation and digital twin integration, deterministic timebase governance, and network I/O determinism. Lume‑Ind aligns deterministic governance with major industrial regulatory frameworks (OSHA, DOT, FAA, ISO, NIST, automotive, robotics safety) and extends the Deterministic Autonomous Infrastructure Governance Systems (DAIGS) category established in the companion Lume‑Med paper into the industrial domain. This work positions Lume‑Ind as the industrial instantiation of a general, cross‑industry deterministic governance architecture built on the Lume programming language and the Lume‑V governance engine.
Ronald Jason Andrews (2026) studied this question.