Multiverse Superposition Inference: Chaos-Seeded Adapter Ensembles with HeartScale Affinity Blending

MSI trains N LoRA adapters on the same frozen base with distinct TERA-space chaos seeds and blends logits using HeartScale affinity scores, achieving ensemble quality at single-model memory cost. Revision 2: per-adapter affinity now uses the resonance / product-Fisher–Rao kernel (cos²(Δφ)·exp(−d_FR/λ)) rather than raw inverse-L2, consistent with WP-15 §2.2 and the AAMT resonance geometry. §6 formalises MSI as the tensor rank-lift of the rank-1 VortexGate (WP-01 §2.2/§7.1): an arithmetic mixture of N product measures is CP-rank ≤ N; logit-space blending is Product-of-Experts with a zero-veto property (one dissenting adapter can veto a token).