When Performance Persists but Identity Fails: A Conceptual Framework for Long-Horizon Adaptive Systems

Adaptive systems are increasingly deployed in environments characterised by uncertainty, non-stationarity, delayed feedback, and cumulative stress. In such settings, failure rarely manifests as abrupt breakdown or immediate loss of functionality. Instead, systems may continue to operate, adapt, and satisfy externally defined objectives while undergoing irreversible internal regime transitions that remain invisible to conventional performance-based evaluation metrics. This work introduces identity continuity as a foundational yet largely unmeasured property of adaptive systems. It argues that prevailing paradigms in reinforcement learning, control theory, and embodied intelligence implicitly assume preservation of internal identity throughout operation—an assumption that breaks down in long-horizon and high-stress environments. The paper reframes robustness as a survival property, defined by the duration over which a system preserves coherent internal organisation rather than by averaged performance indicators such as error, reward, or task success. Identity collapse is treated as a distinct failure mode, fundamentally different from transient degradation, instability, or optimisation inefficiency. The contribution is intentionally conceptual and evaluative. No algorithms, mechanisms, thresholds, or implementation details are disclosed. Instead, the work establishes a principled foundation for identity-aware evaluation of adaptive systems and exposes structural blind spots in existing evaluation practices. The framework described herein may be applied as a standalone evaluative layer or embedded within larger multi-layer architectures, including but not limited to PETRONUS. This publication is intended as prior art and as a conceptual reference point for future research on long-horizon robustness, safety, and identity-preserving adaptive systems.