Language model output probability distributions directly reveal whether trained pathways exist in the model's weight matrices. At prompt tokens corresponding to known concepts, the model's predictions are near-deterministic; at fabricated concepts, predictions scatter across phonetic alternatives (d = 0.84, p 0.5), content authentication (d = 0.93), and manufacturing quality verification (d = 0.90) — all on a general-purpose model with zero domain-specific training. Additional applications include reasoning chain segmentation, through-thickness geometric profiling, training data composition detection, and a continuous memorization spectrum for IP forensics. The pathway specificity mechanism is argued to be structurally tamper-resistant: confident predictions at unfamiliar tokens require trained pathways, making score improvement indistinguishable from model improvement. Injection experiments support this argument, with architecture-dependent partial geometric shifts documented as a boundary condition. These findings support fourteen distinct applications and establish that external geometric measurement and internal mechanistic interpretability are complementary approaches to AI verification.
Joseph Stephens (Wed,) studied this question.