Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights

arXiv:2607.27482v1 Announce Type: new Abstract: A temporally drifting data stream may pass through discrete regimes rather than changing continuously. We ask whether such regimes are recoverable from the weights of models trained on the stream, using a hidden Markov model (HMM) fit to the chronologically ordered trajectory of those weights. We study this question in two domains known to drift over time: multimodal misinformation detection, using the Fakeddit dataset; and sentiment analysis, usin...