Researchers demonstrate that Transformers exhibit linear superposition, where combined inputs from distinct text streams produce outputs that are superpositions of individual next-token distributions. This linearity is intrinsic to the architecture, diminishes during training, but can be restored through fine-tuning, and enables generating two coherent continuations simultaneously from a single forward pass.