Human-Centered Learning Mechanics: A Dynamical Framework for Entropy-Regulated Representation Learning

arXiv:2605.22940v1 Announce Type: new Abstract: Deep learning is increasingly viewed as a dynamical process in parameter space, yet many existing theories still treat training as a closed optimization system. This view is limited for real-world AI, where models operate under uncertainty, resource constraints, distribution shift, downstream decision risks, and human feedback. We propose Human-Centered Learning Mechanics (HCLM), a dynamical and information-theoretic framework for open and controlled learning systems. The central idea is that entropy regularization is useful only when the chosen

Source: arXiv cs.LG — read the full report at the original publisher.

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