New framework details thermodynamics of learning in finite devices

Researchers have introduced a new framework for understanding learning in finite devices, distinguishing between what a device has recorded and what will hold future value. This framework separates learning into four components: training-side fit, record-correlation stock, update-side search ledger, and operational capital value. The research details conditions under which record correlation can increase without a corresponding rise in capital value, and provides identities for capitalization efficiency and value retention under task-distribution shifts. These findings focus on finite-device value retention rather than statistical generalization. AI

RANK_REASON The item is an academic paper detailing a theoretical framework for learning. [lever_c_demoted from research: ic=1 ai=1.0]

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