Grokking - the delayed transition from memorisation to generalisation in neural networks - remains poorly understood. We study this phenomenon through the geometry of learned representations and identify a consistent empirical signature preceding generalisation: collapse of the spectral entropy of the representation covariance matrix. Across modular arithmetic tasks and multiple random seeds, spectral entropy decreases gradually during training and crosses a stable task-specific threshold before test accuracy rises. A representation-mixing intervention that delays this collapse also delays grokking, including under norm-matched controls, indicating that the effect is not explained by parameter norm alone. We further show that the entropy gap predicts the remaining time until grokking with useful out-of-sample accuracy. To probe the structure underlying this transition, we introduce a Fourier-alignment observable for cyclic-group tasks. Entropy collapse is strongly coupled to the emergence of Fourier-aligned representations, suggesting that spectral entropy tracks concentration of the representation into task-structured directions rather than generic compression alone. The same qualitative dynamics appear in non-abelian group composition tasks, while MLP controls show that entropy collapse by itself is insufficient for grokking in the absence of appropriate inductive bias. Taken together, the results support a view of grokking as a representational phase transition with an observable geometric signature. We discuss the scope and limitations of this interpretation, connections to recent feature-learning and spectral-dynamics work, and directions for testing whether similar transitions appear in larger-scale learning systems.