[2605.04054] Endogenous Regime Switching Driven by Scalar-Irreducible Learning Dynamics
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Computer Science > Machine Learning
arXiv:2605.04054 (cs)
[Submitted on 10 Apr 2026]
Title:Endogenous Regime Switching Driven by Scalar-Irreducible Learning Dynamics
Authors:Sheng Ran
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Abstract:Achieving endogenous regime switching is crucial for the emergence of autonomous intelligence, yet remains a central challenge for existing machine learning frameworks, where such transitions are typically externally imposed. In this work, we introduce a classification that distinguishes scalar-reducible dynamics, which can be expressed as gradient flows driven by a scalar objective, from scalar-irreducible dynamics that cannot be reduced to such a form. While most existing machine learning systems operate within the scalar-reducible class, we demonstrate that scalar-irreducible dynamics naturally enable internally generated regime switching through feedback between fast dynamical variables and slow structural adaptation. Using a minimal dynamical model, we illustrate how this mechanism produces sustained endogenous regime transitions without external scheduling. Our results suggest a new dynamical paradigm for regime exploration and provide a potential route toward autonomous learning systems whose adaptive behavior is organized internally rather than externally prescribed.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.04054 [cs.LG]
(or arXiv:2605.04054v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.04054
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arXiv-issued DOI via DataCite
Submission history
From: Sheng Ran [view email] [v1] Fri, 10 Apr 2026 02:40:51 UTC (520 KB)
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