This paper proposes Hybrid Semantic Compiler Architecture as an alternative to "AGI-oracle" framing. It argues that large language models are best understood not as sovereign general intelligences, but as Fuzzy Semantic Compilers and Frontier Semantic Operators: semantic scouts that operate productively where governed closure has not yet been achieved. The paper distinguishes Fuzzy Semantic Compilers, Frontier Semantic Operators, Deterministic Semantic Compilers, and Hybrid Semantic Compilers. It defines Deterministic Semantic Compilers as governed semantic substrates over state: bounded, reproducible, receiptable operators that retrieve, bind, validate, classify, project, admit, refuse, and record semantic material. The central thesis is that advanced AI should not concentrate all intelligence, memory, authority, and capability inside black-box models. Instead, stabilizable capabilities should migrate into deterministic semantic layers, while LLMs remain useful as frontier agents where terms, models, authority, evidence boundaries, or traversal rules remain incomplete. The paper introduces governed closure, crystallization, decrystallization, frontier retreat, weak and strong HSC, hallucination-as-boundary-failure, total cost of closure, bootstrap discipline for governing the crystallizer, and evaluation metrics such as Boundary Violation Rate, GSM Extraction Yield, Frontier Detection Accuracy, Refusal Correctness, and Semantic Downgrade Honesty. It positions Hybrid Semantic Compiler Architecture as a path toward Constitutional Intelligence: intelligence located not merely in model capability, but in governed admissible continuation across humans, models, tools, memory, evidence, receipts, and semantic substrate.
Adam Ableman Mazurk (Mon,) studied this question.