Self-Maintenance as a Consequence of Hierarchical Semantic Constraint

A prior working paper (Mikhailov, 2026, The Semantic Top — hereafter ST) argued that a discovery capable AI benefits from a semantic top: a layered architecture in which compact, low-entropy representational layers constrain and govern a high-entropy computational layer. (Throughout, entropy is used in its standard information-theoretic sense — representational disorder that is defined and, where stated, measured — not as a claim about physics; the thermodynamic parallels ST draws are motivational analogy.) That paper asked why the architecture is useful; this one asks a sharper, falsifiable question: does a system built on the semantic top acquire a capability high-entropy architectures cannot reliably achieve — principled self-maintenance? We answer yes, and argue it is not incidental: self-maintenance is a downstream consequence of hierarchical constraint and deterministic entropy reduction. Each principle postulated in ST is restated as a claim and demonstrated against the code of SEMPL, a constrained-natural-language process engine in SWI-Prolog. SEMPL models its own execution and maintenance as processes it must recover; it reconstructs its human-approval governance gate as a recoverable ordering constraint rather than a declared rule; its explanations of its own behaviour are faithful by construction because they reuse the same derivation machinery; and gap detection over its own specification is driven by complete semantics rather than heuristics. We compare this against autonomic computing / MAPE-K, LLM self-refinement, agentic self-modification, constitutional self-critique, and automated program repair, and identify the axes on which a semantic-top system is structurally better: faithful explanation, recoverable governance, completeness guarantees, and governed promotion. Finally we draw the near-term implication — compositional context produced natively as a by-product of complete-semantics operation — and locate the work on the leading line toward synthesized AI.