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Under review as a conference paper at ICLR 2027

Styles Are Islands: The Discrete Geography of Music Generation and Why Steering Is One-Way

Abstract

Text-to-music models now write usable music from a sentence. Steering functions give such models a second control. A direction is added to a hidden state, or an affine map is applied to it, and an attribute of the music is dialed up or down without touching the prompt. This is the control a user needs when the request is “more of this” rather than a sentence, or when the model takes no prompt at all. The family has worked well for removing attributes, and it carries an unstated assumption: the control that removes a style also adds it. We test this assumption on a text-to-music model with an automatic listener as the judge, and it fails. The same steering vector removes a style almost completely and adds it hardly at all. Removal recovers most of the distance between the two styles, addition about a tenth, and the two dose curves enclose a hysteresis loop. The natural repair is an affine map that matches the shape of the target class. It is more faithful to the class statistics and fails in the same way, as do re-timed pushes and pushes optimized directly against the listener. We resolve the conflict by asking what a style is in state space. A style is not a region but a thin set of trajectories that occupies no volume. Leaving such a set is certain under any push, and landing on it is impossible for every steering function, affine maps included. Transplanting a real state of the target style reaches the style, whereas the same state restricted to its most discriminating directions does not. The diagnosis yields the method. Transport the state to the target class with the optimal affine map, then project it onto the nearest real trajectory of that class and follow it. Neither step suffices alone. Together they close the loop on electronic texture at both model sizes and part of the way on jazz, with the music label preserved, where the steering vector reaches a tenth of the way.

open until 14 Dec 2026

est. 32% chance this paper gets accepted at ICLR 2027.

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