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 \textit{Spectral 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 \textit{Structural Disintegration} (Regime I), characterized by a $13.4\%$ collapse in Cross-Attention rank. Conversely, large models enter a \textit{Compression-Seeki...
| Authors | Ivan Viakhirev, Kirill Borodin, Grach Mkrtchian |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 26 June 2026 |
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