Admissibility Before Optimization: Authorization Layers for Long-Horizon Adaptive Systems

This work introduces and formalizes admissibility as a first-class architectural variable in long-horizon adaptive systems. Most modern adaptive and learning-based systems are built around optimization: actions are selected by maximizing expected utility, reward, or performance. This paradigm implicitly assumes that all actions are admissible, and that undesirable outcomes can be mitigated through improved objectives, deeper planning, or better uncertainty estimation. While effective in short-horizon or reversible settings, this assumption fails in systems where actions carry irreversible structural consequences over time. The central claim of this work is that long-horizon failure is often not caused by suboptimal choice, but by unauthorized commitment: irreversible transitions executed before the system is structurally ready to collapse uncertainty into commitment. Once such transitions occur, no amount of subsequent optimization can recover the lost admissible space. To address this, the paper isolates an authorization layer that precedes optimization. Admissibility is formalized as a binary authorization function that determines whether an action is permitted to occur at a given internal stage of system evolution. This layer does not optimize, plan, or correct behavior. It operates independently of reward, risk, or task objectives and governs access to irreversible transitions based on cumulative structural impact over internal time. The contribution is intentionally architectural rather than algorithmic. The paper does not propose a new controller, learning rule, or solver, nor does it provide an end-to-end system implementation. Instead, it establishes a minimal formal interface that demonstrates why admissibility cannot be reduced to constrained optimization, reward shaping, or state augmentation without loss of essential information. By making authorization explicit and separating it from optimization, this work identifies a missing architectural layer necessary for preserving long-horizon viability, identity continuity, and structural coherence in adaptive systems operating under irreversibility. Cybernetics 2.5