Enactive Drift Regulation and the Emergence Machine: A Framework for Coherent Adaptation Through Regulated Interaction

Abstract

Adaptive systems increasingly operate in environments characterized by persistent non-stationarity, where patterns reorganize rather than merely vary. While existing approaches such as online learning, continual learning, and adaptive filtering address performance degradation under changing data distributions, they typically treat drift as noise, error, or distribution shift to be corrected. This paper argues that such framings miss a more fundamental challenge: the loss of organizational coherence over time. We introduce Enactive Drift Regulation (EDR) as a general adaptive principle that treats drift as a regulatory signal indicating breakdowns in coherence between a system's internal organization and its environment. Rather than treating prediction optimization or retraining as sufficient, EDR reframes adaptation as the regulation of structure-maintaining, reorganizing, or transitioning internal dynamics to sustain viable operation under change. We present the Emergence Machine as an architectural instantiation of EDR, organized around regimes, attractors, coherence measures, reorganization dynamics, and memory across regimes. By shifting the focus from error minimization to coherence regulation, this work provides a principled framework for long-duration adaptation under non-stationarity and offers a bridge between adaptive control and enactive accounts of cognition.