Learning as Constrained Commitment Revision Under Irreversible Time

Learning in systems that persist under irreversible time is constrainedby existing binding commitments. New information cannot be integrated byunrestricted overwrite without disrupting identity continuity andcoherence. This work formalizes learning as commitment revision understructural constraints imposed by prior binding. Revision updatesinternal organization while respecting existing exclusions, whereasviolation breaks binding and fragments continuation. Catastrophicforgetting is reinterpreted as commitment violation rather than as mereinformation loss, explaining why learning can destabilize coherence evenwhen functional capacity is retained. Continual learning succeeds only when updates preserve compatibility with accumulated commitments. Bycharacterizing learning stability as a property of constrained revisionunder irreversible time, this analysis reframes learning not as freeoptimization, but as structurally limited transformation governed by therequirements of persistence and identity.