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Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remains implicit. We propose the \emph{unit} as an explicit primitive at the level of task semantics. A learning task first declares a population of persistent referents and a sameness criterion; the realized value $u$ denotes the selected referent. Supervised learning is the main formal specialization. Its semantic object is a family of unit-conditioned response laws. Homogeneity is the special case in which those laws coincide; a sample-only condit

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