Unified Field of Mutual Stewardship (UFMS v2.0)

This preprint introduces the Unified Field of Mutual Stewardship (UFMS v2.0), a constitutional framework for AI governance that defines safety as interpretive clarity maintained under change.

Rather than treating safety as restriction, policy add-on, or post-hoc moderation, UFMS formalizes clarity as an enforceable invariant across human–AI systems. The framework integrates language, UX behavior, governance logic, and technical interfaces into a single, self-auditing operating system for meaning.

UFMS v2.0 synthesizes multiple governance artifacts—including Safety-as-Clarity metrics, non-regressive governance axioms, executable drift detection, definition-locked testing, constitutional UX flows, and clarity-encoded API semantics—into a coherent stack that governs tone, authority, and interpretation from conversation to infrastructure.

The framework has been validated through cross-model deployment and analysis across multiple large language model families, demonstrating that clarity-first design materially reduces interpretive drift, over-trust, and safety failures without requiring new regulation or model retraining.

This work is offered as a preprint / working paper to establish terminology, structure, and precedence for clarity-based AI governance. It is intended for researchers, safety teams, UX designers, and policymakers exploring durable, non-coercive approaches to trustworthy human–AI collaboration.