🤖 AI Summary
This work addresses a fundamental limitation in existing adaptive methods, which treat environmental non-stationarity—particularly drift—as mere noise or distributional shift, thereby overlooking the progressive loss of organizational coherence between system and environment over time. To overcome this, the paper introduces the principle of Egregious Drift Regulation (EDR), reframing drift as a regulatory signal of coherence mismatch. EDR enables long-term coherent adaptation by dynamically adjusting the system’s internal structure to maintain, reorganize, or transition its operational mechanisms. Departing from conventional error-minimization objectives, this approach shifts the adaptive goal toward coherence regulation, integrating adaptive control with embodied cognition theory. It realizes a mechanism-centered “emergent machine” architecture that unifies state mechanisms, attractor dynamics, coherence metrics, reconfiguration dynamics, and cross-mechanism memory. The resulting framework offers a principled solution for intelligent systems operating in persistently non-stationary environments, substantially enhancing their long-term functional coherence.
📝 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.