From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales

Hallucinations in large ASR models present a critical safety risk. In this work, we propose the textitSpectral Sensitivity Theorem, which predicts a phase transition in deep networks from a dispersive regime (signal decay) to an attractor regime (rank-1 collapse) governed by layer-wise gain and alignment. We validate this theory by analyzing the eigenspectra of activation graphs in Whisper models (Tiny to Large-v3-Turbo) under adversarial stress. Our results confirm the theoretical prediction: intermediate models exhibit textitStructural Disintegration (Regime I), characterized by a 13.4% collapse in Cross-Attention rank. Conversely, large models enter a textitCompression-Seeking Attractor state (Regime II), where Self-Attention actively compresses rank (-2.34%) and hardens the spectral slope, decoupling the model from acoustic evidence.