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Methodological synthesis demonstrates reference-free, zero-GPU structural auditing for AI outputs, highlighting external validation mechanisms beyond black-box evaluation.
This deposit presents a technical synthesis of the Symbiotic Autoprotection System (SAS), a research line dedicated to reference-free, zero-GPU structural auditing of AI-generated text and code. The document articulates eight milestones of documented experimental work (R0 → R2.1-b), including: the Invariant Similarity Index (ISI) with validated thresholds κD = 0.56 and κR = 0.15; a seven-module multimetric tribunal with cascading multiplicative penalty; SourceTargetGuard for critical-entity mutation detection; and reference-free Python API validation via sandboxed introspection. It explains what structural auditing is, why an external auditable layer is necessary, what was built, what the technical and scientific implications are, and what limits remain. The tone is deliberately sober: no universal superiority claims, no attacks on existing approaches, and no messianic language. This is a synthesis document, not a primary experimental report. All underlying experimental data, code, and per-milestone results are deposited separately under their own DOIs (see References). License: Durante Invariance License v1.0Registry: TAD EX-2026-18792778
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Gonzalo Emir Durante (2026) studied this question.
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