Music Circuit: Trace and Control Style Dynamics in Large Music Generation Model

Musical style, a global attribute that functions throughout a whole piece, is a lever widely used by users in modern large music generation models. Yet how style is represented inside these models, and which components drive it, remains largely unexplored. In language models, circuit analysis traces a representation to a sparse sub-network of a few key components, namely attention heads and MLP neurons, and has been used successfully to explain and control global attributes such as emotion. In this work, we use circuit analysis to study the mechanisms behind musical style. Based on YuE2, we build a dataset that pairs the same lyrics with five styles and design a three-step framework. First, stage localization shows that the style forms mainly in the score-planning stage. Second, static observation across the positions of this stage further reveals that style is not a fixed vector but a dynamic representation across tokens. Third, we therefore merge the activation differences across positions into one direction per style, and trace the neurons and attention heads that drive this direction into a sparse circuit. Without any style text, steering this circuit successfully transfers one piece into the target style, matching and even exceeding the style prompt. To our knowledge, this is the first work to analyze and control musical style at the circuit level in a large music generation model. Audio and score examples are available at https://aristo233.github.io/Music_Circuit/.