General Machine Learning: Theory for Learning Under Variable Regimes

• URL: http://arxiv.org/abs/2603.23220v1
• Date: Tue, 24 Mar 2026 13:58:31 GMT
• Title: General Machine Learning: Theory for Learning Under Variable Regimes
• Abstract summary: We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time.<n>The paper develops a regime-varying framework centered on admissible transport, protected-core preservation, and evaluator-aware learning evolution.
• Score: 2.538209532048866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contribution: its goal is to define the core learning-theoretic objects required for such settings and to establish their first theorem-supporting consequences. The paper develops a regime-varying framework centered on admissible transport, protected-core preservation, and evaluator-aware learning evolution. It records the immediate closure consequences of admissibility, develops a structural obstruction argument for faithful fixed-ontology reduction in genuinely multi-regime settings, and introduces a protected-stability template together with explicit numerical and symbolic witnesses on controlled subclasses, including convex and deductive settings. It also establishes theorem-layer results on evaluator factorization, morphisms, composition, and partial kernel-level alignment across semantically commensurable layers. A worked two-regime example makes the admissibility certificate, protected evaluative core, and regime-variation cost explicit on a controlled subclass. The symbolic component is deliberately restricted in scope: the paper establishes a first kernel-level compatibility result together with a controlled monotonic deductive witness. The manuscript should therefore be read as introducing a structured learning-theoretic framework for regime-varying learning together with its first theorem-supporting layer, not as a complete quantitative theory of all learning systems.

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