arXiv:2608.29706v2 Announce Type: replace Abstract: A token's representation is carried through the network layer by layer. The whole vocabulary carried together forms a flow. We fit this flow's equation of motion as a discrete Langevin model over corpus-mean trajectories of Pythia-160M and Pythia-410M, and score the predicted steps on held-out tokens. Linear maps are often used as cheap surrogates for a layer. The flow they summarize is not linear: a quadratic drift beats the linear map at every transition of both models, and the Kramers-Moyal estimator agrees wherever its neighborhoods stay local. At full width, the step of a mean trajectory is nearly affine, 3% non-affine, while the step of an individual state is not, 17%. We then characterize the flow further. First, we show that it does not descend its own log-density, whatever the diffusion matrix. The drift instead descends a potential that is not the density. Second, the rotational component is not negligible, 4 to 45% of the explainable drift, and it is the antisymmetric part of the linear map. Finally, a token keeps its angular rank and its local-density rank across all thirteen layers, on two corpora and both models.