Specification Without Loss Constraint-Driven Meaning and Implied Generativity in Computational Systems

Conventional computational systems treat outputs as atomic facts, implicitly assuming that meaning is preserved through transmission or degraded through loss. This paper proposes an alternative framing: specification as a process of progressive constraint rather than information loss. We formalize the principle that every concrete specification implicitly implies the full generative space from which it was selected. Building on this axiom, we introduce a specification entropy metric to quantify commitment depth and propose a Shadow-Alphabet Module that preserves awareness of excluded alternatives during decision-making. This framework enables systems that calibrate confidence, delay action appropriately, and maintain interpretability without relying on probabilistic inference or model introspection.