What if a single forward pass could let a model read two completely different texts at once? Transformers are built from strongly nonlinear pieces: self-attention and layer after layer of MLPs. So the natural intuition is that mixing two contexts into one input should make the output collapse into noise unrelated to either. This paper overturns that intuition. Simply averaging the token embeddings of two texts and feeding the result as a single input still leaves clear traces of both contexts in the next-token distribution. Tested across Pythia, Llama, and Qwen, the true next token from each individual stream lands in the top-10 ranks of the mixed output 30-40% of the time, and within the top-100 ranks 60-65% of the time. Even more striking: this superposition ability isn't something models learn. It's strongest right at initialization and degrades monotonically as pretraining continues, suggesting it's an intrinsic architectural property that training actually erodes. The authors show it can be substantially restored with lightweight fine-tuning on less than 0.025% of the original pretraining data, and they build on this to propose a guided decoding method that generates two independent, coherent continuations from a single forward pass. Title: Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs URL: #LLM# #Transformers#