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arXiv · 2507.22255

Agent-centric learning: from external reward maximization to internal knowledge curation

Hanqi Zhou·Fryderyk Mantiuk·David G. Nagy·Charley M. Wu
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Abstract

The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures. We posit that the capacity -- to shape one's own understanding -- is an element for achieving better ``preparedness'' distinct from direct environmental influence. Focusing on internal representations as the main substrate for computing empowerment offers a new lens through which to design adaptable intelligent systems.

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Hanqi Zhou, Fryderyk Mantiuk, David G. Nagy, Charley M. Wu. 2025-07-29. Agent-centric learning: from external reward maximization to internal knowledge curation. https://arxiv.org/abs/2507.22255

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