Semantic Commitment as a Structural Constraint in Long-Horizon Adaptive Systems

Adaptive systems deployed in real-world environments increasingly operate under conditions of uncertainty, partial observability, delayed feedback, and irreversible consequences. While contemporary approaches achieve high performance through improved perception, probabilistic inference, and optimization, long-horizon failures continue to occur even when observations are correct, behavior is locally admissible, and no adversarial interference is present. This work identifies and formalizes a distinct architectural failure mode—context capture—in which transient contextual salience induces premature semantic commitment, allowing unstable interpretations to authorize irreversible structural operations. Such failures cannot be resolved by increasing model accuracy, confidence calibration, or reward optimization, as they arise from a structural mismatch between semantic interpretation and semantic commitment rather than from inferential error. The paper introduces an architectural framework that explicitly separates semantic interpretation from semantic commitment and treats commitment as a governed authorization process rather than an inferential outcome. Commitment admissibility is regulated through an internal temporal notion distinct from wall-clock time and instantaneous confidence, while organizational regime transitions are treated as protected structural operations incurring irreducible cost. Long-horizon viability and identity preservation are achieved through architectural constraint rather than direct optimization. The framework is presented at the level of architectural principles and invariants, without specifying algorithms, thresholds, or implementation details. As such, it is intended to serve as prior art for a class of adaptive and autonomous systems requiring semantic safety under contextual uncertainty, including artificial agents, robotic platforms, distributed decision systems, and cyber-physical systems.