LLM Hidden-Activation Style Analysis Paper

An arXiv research paper proposing a method to discover prompt-conditioned style axes from LLM hidden activations by combining high-temperature sampling with PCA dimensionality reduction. The approach requires neither supervised training nor human annotation to identify the principal directions of stylistic variation within a model's internal representations.