Large Language Models (LLMs) are typically framed as probabilistic text predictors whose failures are attributed to hallucination, misalignment, or insufficient training data. This paper advances a different account: many of the most damaging failures in LLM deployment arise not from model incapacity, but from ungoverned continuation—situations in which speculative language is allowed to acquire binding authority without explicit admissibility, audit, or refusal semantics. We introduce Governed Semantic Compilation (GSC) as a systems-level framework for embedding LLMs in a compiler-like interaction regime. In GSC, model outputs are treated as proposed state transitions over an explicitly governed semantic state. Transitions that satisfy declared admissibility constraints may be promoted; those that do not are treated as undefined, with refusal reclassified as a compile-time success condition rather than a failure. The paper formalizes GSC via a minimal transition model, introduces the concept of epistemic promotion (the moment when text becomes authoritative state), and reframes hallucination as a governance failure rather than a knowledge defect. We argue that governed regimes yield higher coherence, stability, auditability, and resistance to thrash—not only in high-stakes deployments, but also in creative exploration and agentic coding workflows, where explicit state and refusal enable cleaner backtracking and reduced churn. Rather than proposing a new model architecture or alignment objective, this work focuses on interface discipline: how and when LLM outputs are allowed to count. GSC is presented as an architectural lens and design pattern applicable across domains including coding agents, decision support systems, governance tooling, and human–AI collaboration.
Adam Ableman Mazurk (2026) studied this question.