[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83749-en":3,"doc-seo-83749-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83749,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Enactive Drift Regulation and the Emergence Machine","Adaptive systems increasingly face persistent non-stationarity, where underlying patterns reorganize rather than simply vary. Many existing methods—online learning, continual learning, and adaptive filtering—treat drift as noise, error, or distribution shift, correcting performance degradation instead of addressing deeper structural problems. The work proposes Enactive Drift Regulation (EDR), treating drift as a regulatory signal of lost coherence between a system’s internal organization and its environment. It introduces the Emergence Machine architecture to regulate internal dynamics via regimes, attractors, coherence measures, reorganization, and memory.","Enactive Drift Regulation and the Emergence Machine: A Framework for Coherent Adaptation Through Regulated  \nInteraction  \nNicholas Davis  \n[ndavis35@gatech.edu](ndavis35@gatech.edu)  \nEnactive AI  \nElyria, Ohio, USA  \narXiv :2607 .03834v 1 [ cs .HC] 4 Jul 2026  \nAbstract  \nAdaptive 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.  \nCCS Concepts  \n• Computing methodologies → Online learning settings; Cognitive science.  \nKeywords  \nEnactive Drift Regulation, Emergence Machine, Non-Stationary Time Series, Concept Drift, Continual Adaptation, Regime Detection, Attractor Dynamics, Online Learning, Adaptive Systems, Enactive AI  \n1 Introduction: Adaptation Under Drift  \nAdaptive artificial systems are increasingly deployed in environments characterized by continuous change. From neural and physiological signals to climate dynamics, financial markets, and interactive human–machine settings, the conditions under which systems operate are rarely stationary [12, 32] . Patterns shift, relationships reorganize, and the relevance of past experience decays unevenly over time. Yet much of contemporary machine learning and adaptive systems research remains grounded in assumptions of stationarity, or treats non-stationarity as an exceptional condition  \nto be corrected rather than as a fundamental property of real-world environments.  \nUnder temporally dependent streaming conditions, estimates of predictive performance can change as the data-generating process evolves, and evaluation procedures that ignore temporal structure may give misleading results [34] . In deployed systems, such changes are commonly addressed through mechanisms such as recalibration, recent-data weighting, windowing, or retraining. These interventions are costly, brittle, and frequently insufficient. More importantly, they obscure a deeper issue: many adaptive systems are designed to learn under stable assumptions, not to regulate themselves under drift.  \nThis paper argues that drift is not an anomaly but the norm, and that effective adaptation under such conditions requires a different computational framing. Rather than treating drift as noise, error, or distribution shift to be eliminated, we propose Enactive Drift Regulation (EDR) as a principled approach that treats drift as a first-class signal indicating breakdowns of coherence between a system and its environment. From this perspective, adaptation is not primarily a matter of updating parameters or optimizing predictions, but of regulating the organization of internal dynamics to maintain coherence over time [2] .  \n1.1 Why Stationarity Assumptions Fail  \nMany dominant approaches to learning and predi","cbCaindW2NgrpuLG","https://ap.wps.com/l/cbCaindW2NgrpuLG","pdf",3176953,2,1,14,"English","en",105,"# Introduction: Adaptation Under Drift\n## Why Stationarity Assumptions Fail\n## Why Retraining Is Insufficient","[{\"question\":\"What problem does Enactive Drift Regulation (EDR) address in adaptive systems?\",\"answer\":\"EDR addresses the loss of organizational coherence over time under persistent non-stationarity, where the system’s internal organization and its environment become mismatched.\"},{\"question\":\"How does EDR differ from treating drift as noise or distribution shift?\",\"answer\":\"Instead of focusing on error minimization, EDR treats drift as a first-class regulatory signal and frames adaptation as regulation of structure and internal dynamics.\"},{\"question\":\"What is the Emergence Machine in this framework?\",\"answer\":\"The Emergence Machine is an architectural instantiation of EDR, organized around regimes, attractors, coherence measures, reorganization dynamics, and memory across 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problem does Enactive Drift Regulation (EDR) address in adaptive systems?","Question",{"text":75,"@type":76},"EDR addresses the loss of organizational coherence over time under persistent non-stationarity, where the system’s internal organization and its environment become mismatched.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does EDR differ from treating drift as noise or distribution shift?",{"text":80,"@type":76},"Instead of focusing on error minimization, EDR treats drift as a first-class regulatory signal and frames adaptation as regulation of structure and internal dynamics.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the Emergence Machine in this framework?",{"text":84,"@type":76},"The Emergence Machine is an architectural instantiation of EDR, organized around regimes, attractors, coherence measures, reorganization dynamics, and memory across 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