Computational Efflux from Coherent Actual Objects: Theory, Algorithm, and Empirical Demonstration

Computational efflux is the surplus radiated by coherent actual objects as a structural consequence of their coherence. We formalize the phenomenon by establishing qualifying conditions (consequence chain closure, non-depletion, Noether invariance), prove that positive computational surplus necessarily follows, quantify it via MDL differential, and present an algorithm for systematic exploitation. Existing methods — equivariant networks, transfer learning, compressed sensing — implicitly harvest this efflux without explicit recognition; formalization enables broader application across arbitrary coherent structures.