E3 Hallucination as Tensor Failure Not Model Error

This paper reclassifies hallucination in artificial systems as a tensor-level standing failure rather than as a model defect, data issue, or optimization error. Under admissibility and standing conservation, hallucination is shown to arise when load-bearing tensor structure is violated or overstressed, irrespective of surface performance or statistical confidence. The argument proceeds by exhaustion: all attempts to treat hallucination as correctable through tuning, training, or post-hoc filtering collapse into illegitimate repair strategies. The analysis is declarative and non-procedural, placing on record a structural diagnosis of hallucination without introducing mitigation algorithms, detection heuristics, or operational fixes.