Conservation of Differentiation: The MOSAIC Framework for Compression Without Collapse

Compression is unavoidable in practice. Scientific models, machine-learning systems, legal records, institutions, narratives, and metaphysical theories all reduce the complexity of the domains they represent. But compression becomes dangerous when it erases distinctions that remain relevant for prediction, intervention, accountability, ethical response, or future revision. This paper proposes the MOSAIC framework as a formal-methodological synthesis for making declared collapse criteria auditable and for surfacing registered, modeled, or witnessable collapse candidates relative to declared, inspectable, and contestable contexts, organized around a programmatic principle we call Conservation of Differentiation. The name is not meant as a physical conservation law: no conserved scalar quantity, invariant measure, or physical dynamics is claimed here.