Structural Strategies for Avoiding Destructive Interference in Continually Adaptive Systems

This paper presents an independent theoretical analysis of how continually adaptive systems manage the problem of destructive interference while preserving functional coherence over time. In artificial neural networks, this challenge is formalized as catastrophic forgetting and addressed through mechanisms such as replay, regularization, and sparsity. In embodied cognitive systems, analogous failures appear as experiential interference, affective overload, and loss of adaptive flexibility. Rather than treating these domains as incommensurable, the paper examines them through a comparative structural lens. The analysis identifies recurring organizational strategies that emerge under sustained adaptive pressure, including replay-like reactivation of prior states, constraint-mediated plasticity, representational or experiential load reduction, and higher-order regulation of learning dynamics. The paper does not assert ontological equivalence or shared causal substrates, and it avoids metaphysical or normative claims. Its contribution is a non-reductive framework for cross-domain structural comparison, with implications for continual learning, embodied cognition, adaptive system design, agency, and long-horizon alignment.