Davis' EDR framework redefines AI adaptation with coherence regulation

Adaptive systems like online learning and continual learning typically treat drift as noise or distribution shift to correct. But in environments with persistent non-stationarity—where patterns reorganize rather than merely vary—this framing misses a deeper problem: loss of organizational coherence over time. Nicholas Davis's new paper introduces Enactive Drift Regulation (EDR) as a general adaptive principle that reframes drift as a regulatory signal indicating breakdowns in coherence between a system's internal organization and its environment. Rather than optimizing prediction or retraining, EDR views adaptation as the regulation of structure-maintaining, reorganizing, or transitioning internal dynamics to sustain viable operation under change.

The paper presents the Emergence Machine as an architectural instantiation of EDR. It is organized around five components: regimes (stable behavioral modes), attractors (preferred dynamics), coherence measures (quantifying alignment between system and environment), reorganization dynamics (triggered when coherence degrades), and memory across regimes (allowing recovery of past configurations). This design contrasts with error-minimization approaches by prioritizing coherence regulation over prediction optimization. The framework also offers a bridge between adaptive control and enactive accounts of cognition—a perspective that treats autonomous systems as embodied agents that generate meaning through interaction with their environment. While still theoretical, the work provides a principled foundation for building AI systems that can maintain stability and coherence during long-duration deployment in ever-changing real-world conditions.