The Missing Paradigm in AI Governance: Why Inference Time Control Must Replace Training Only Alignment

This paper introduces “inference‑time constitutional governance” as a missing paradigm in modern AI safety. Current governance strategies overwhelmingly rely on training‑time alignment—embedding safety and values into model parameters before deployment—yet this approach cannot anticipate dynamic environments, novel contexts, or multi‑engine interactions. As AI systems become more integrated, tool‑using, and context‑dependent, static training‑only alignment fails to provide real‑time oversight or conflict resolution.

The paper argues that safe, reliable AI requires a hierarchical, rule‑based governance layer that operates during inference. This constitutional system provides real‑time arbitration, drift detection, rule enforcement, and context‑aware decision control without modifying model weights. A case study demonstrates how multi‑engine architectures can be governed through precedence rules, verification membranes, and inference‑time arbitration. The work concludes with a research agenda outlining the theoretical, technical, and evaluative foundations needed to formalize constitutional inference as a new direction in AI governance.