Research demonstrates that pre-trained word embeddings evolve through a continuous dynamical system into a stable equilibrium, marked by four invariant statistical properties. Over 5,000 integration steps using GloVe 300d embeddings, this process amplifies semantic relationships between words by approximately fourfold compared to controlled baselines. The dynamical framework operates independently of traditional optimization methods, Transformers, or RNNs, relying solely on fixed vector field dynamics. The system’s convergence suggests intrinsic geometric constraints govern word vector representations regardless of initialization.