This repository introduces a conservation law for commitment in language under transformative compression and recursive application. We formalize commitment as an information-bearing invariant that must be preserved across paraphrase, summarization, and iterative reuse, even as surface form and representation change. We propose a falsifiability framework that operationalizes this invariant using compression-based stress tests and lineage-aware evaluation, distinguishing semantic preservation from mere token retention. The framework is designed to be model-agnostic and applicable to both human and machine-generated language. This disclosure presents the theoretical law, evaluation criteria, and architectural relationships. Implementation mechanisms are outside the scope of this paper.