Metamorphic System Recomposition

This paper introduces a formal architecture for metamorphic system recomposition: a class of transformations in which a system must temporarily lose its prior identity, undergo irreversible internal reorganization, and re-emerge as a non-reducible new structure — without reliance on external control or incremental optimization. Unlike conventional adaptive or learning systems, metamorphic recomposition requires explicit breakdown, protected uncertainty intervals, invariant preservation, and post-collapse reconstruction under a new topology. We present a minimal formal grammar for such transformations, define necessary and sufficient conditions for true recomposition, and propose verification criteria that prevent false integration, premature coherence, or retroactive redefinition of success. The framework is domain-agnostic and applicable to artificial intelligence, software architecture, multi-agent systems, governance mechanisms, and human–machine hybrid systems. This work establishes conceptual priority for a distinct class of system transformations not adequately captured by existing models of learning, optimization, or control.