Feature Geometry of Language Models Transfer Across Modalities to Time Series

Language models transfer to time-series forecasting, but it is unclear whether this reflects reusable internal structure or rapid relearning under a familiar architecture. We study this transfer directly by comparing pretrained and randomly initialized versions of the same model on a forecasting objective whose inputs have little semantic overlap with text but still require autoregressive sequential structure. Across Qwen3-0.6B finetuning experiments, language initialization gives coherent per-example gradients from the first update, while random initialization first passes through a low-alignment warmup phase. Effective-rank and hidden-state analyses show that finetuning selectively reshapes an existing representation geometry rather than constructing the simpler temporal geometry found by models trained from scratch. Cross-domain sparse features and causal ablations then expose candidate transferred primitives, including a Layer~1 head--MLP circuit whose ablation selectively increases loss on periodic forecasting and repetitive language passages. These results support an account of cross-modal transfer in which autoregressive pretraining creates temporal feature geometry that can be selected and specialized outside language.