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
This publication represents a growing academic and industry recognition of the limitations of purely optimization-driven AI development, particularly as AI models are deployed in complex, real-world scenarios requiring adaptation and human interaction.
A strategic reader should care because this framework proposes a foundational shift in how AI systems are designed and trained, moving towards more adaptable, controlled, and human-aware learning, which is crucial for safety, reliability, and broad adoption.
The focus shifts from closed-system optimization to open, dynamical, and information-theoretically regulated learning, incorporating aspects like uncertainty, resource constraints, and human feedback directly into the learning mechanics.
- · AI safety researchers
- · AI developers in complex, uncertain environments
- · Robotics and autonomous systems
- · Ethical AI initiatives
- · Purely optimization-focused AI paradigms
- · Companies with opaque or uncontrollable AI systems
AI models will become inherently more robust and adaptable to real-world deployment challenges, including distribution shifts and resource limitations.
This could accelerate the integration of AI into critical infrastructure and sensitive decision-making processes by addressing existing concerns about unpredictable behavior.
It might lead to new regulatory frameworks for AI that mandate specific 'human-centered' learning principles, fostering a more responsible AI ecosystem.
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Read at arXiv cs.LG