Semantic Relativity Theory v3.3P

Semantic Relativity Theory v3.3P extends the observer-conditional framework of TRS v3.2P into structured AI classification systems whose outputs can activate operational branches. The paper distinguishes schema conformance from execution repeatability, reformulation invariance, material-change sensitivity, and observer-conditioned operational risk. Its empirical grounding comprises 41 controlled texts and 287 cleaned observations across three tested AI configurations. The results show that schema-valid outputs can coexist with decision changes under equivalent reformulation, repeated execution, and cross-system comparison. The publication reports the conceptual extension, aggregate empirical results, limitations, and experimental-governance requirements. Operational formulas, weights, thresholds, calibration procedures, protected corpora, scripts, case-level outputs, and decision boundaries remain outside the public disclosure boundary.