From Prerequisites to Predictions: Validating a Geometric Hallucination Taxonomy Through Controlled Induction

We test whether a geometric hallucination taxonomy -- classifying failures as center-drift (Type~1), wrong-well convergence (Type~2), or coverage gaps (Type~3) -- can distinguish hallucination types through controlled induction in GPT-2. Using a two-level statistical design with prompts (N = 15/group) as the unit of inference, we run each experiment 20 times with different generation seeds to quantify result stability. In static embeddings, Type~3 norm separation is robust (significant in 18/20 runs, Holm-corrected in 14/20, median r = +0.61). In contextual hidden states, the Type~3 norm effect direction is stable (19/20 runs) but underpowered at N = 15 (significant in 4/20, median r = -0.28). Types~1 and~2 do not separate in either space (≤,3/20 runs). Token-level tests inflate significance by 4--16× through pseudoreplication -- a finding replicated across all 20 runs. The results establish coverage-gap hallucinations as the most geometrically distinctive failure mode, carried by magnitude rather than direction, and confirm the Type~1/2 non-separation as genuine at 124M parameters.