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Discovering and Steering Interpretable Concepts in Large Generative Music Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
InterpretabilityGenerative ModelsMusicMechanisticSparse Autoencoders
Abstract:

The fidelity with which neural networks can now generate content such as music presents a scientific opportunity: these systems appear to have learned implicit theories of such content's structure through statistical learning alone. This offers a potentially new lens on theories of human-generated media. When internal representations align with traditional constructs (e.g. chord progressions in music), they show how such categories can emerge from statistical regularities; when they diverge, they expose limits of existing frameworks and patterns we may have overlooked but that nonetheless carry explanatory power. In this paper, focusing on music generators, we introduce a method for discovering interpretable concepts using sparse autoencoders (SAEs), extracting interpretable features from the residual stream of a transformer model. We make this approach scalable and evaluable using automated labeling and validation pipelines. Our results reveal both familiar musical concepts and coherent but uncodified patterns lacking clear counterparts in theory or language. As an extension, we show such concepts can be used to steer model generations. Beyond improving model transparency, our work provides an empirical tool for uncovering organizing principles that have eluded traditional methods of analysis and synthesis.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

This paper introduces a method for discovering interpretable musical concepts in generative music models using sparse autoencoders, with automated labeling and validation pipelines. It resides in the 'Music Generation Model Analysis' leaf, which contains only two papers total (including this one). This represents a notably sparse research direction within the broader taxonomy of nine papers across multiple audio and speech interpretability domains. The limited population of this specific leaf suggests the work addresses a relatively nascent application area for sparse autoencoder techniques.

The taxonomy reveals that while sparse autoencoders have been applied to adjacent domains—audio foundation models, speech emotion recognition, and clinical biomarkers—the specific focus on generative music models remains underdeveloped. Neighboring leaves include 'Audio Foundation Model Interpretability' (covering singing technique classification and general audio understanding) and 'Diffusion Process Concept Evolution' (tracking feature emergence across timesteps). The paper's emphasis on music-specific concepts like chord progressions distinguishes it from these broader audio applications, though methodological overlap exists in the core sparse coding approach.

Among thirty candidates examined, none clearly refuted any of the three main contributions: the unsupervised concept discovery pipeline (ten candidates examined, zero refutable), the automated evaluation framework (ten examined, zero refutable), and feature steering for controllable generation (ten examined, zero refutable). The single sibling paper in the same taxonomy leaf shares the sparse autoencoder methodology but appears to emphasize different aspects of music model analysis. This limited search scope suggests that within the examined literature, the specific combination of automated validation pipelines and steering mechanisms for music generation represents relatively unexplored territory.

Based on the top-thirty semantic matches examined, the work appears to occupy a distinctive position combining music-specific interpretability with scalable automation. However, the small taxonomy size and limited search scope mean this assessment reflects only a narrow slice of potentially relevant literature. The analysis does not cover exhaustive prior work in music information retrieval, general mechanistic interpretability, or broader audio generation research that might contain overlapping ideas under different framing.

Taxonomy

Core-task Taxonomy Papers
9
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Discovering interpretable concepts in generative music models using sparse autoencoders. The field of interpretability for generative models has recently expanded beyond language and vision to encompass audio and music domains, with researchers applying sparse coding techniques to uncover meaningful latent structures. The taxonomy reveals four main branches: Music and Audio Generation Interpretability focuses on understanding creative generative systems for music and general audio; Diffusion Model Interpretability examines the internal representations of diffusion-based architectures; Speech Analysis and Biomedical Applications targets domain-specific interpretability in voice and clinical contexts; and Non-Audio Embedding Interpretability explores interpretability methods for embeddings outside the audio modality. Works like Interpretable Music Concepts[1] and Audio Latent Features[3] exemplify efforts to decode what generative models learn about musical structure, while Sparse Autoencoders Diffusion[2] and Sparse Interpretable Codec[7] demonstrate how sparse decomposition can reveal interpretable directions in latent spaces across different model families. A particularly active line of work centers on applying sparse autoencoders to music generation models, where researchers seek to identify and manipulate high-level musical concepts such as rhythm, harmony, and timbre. Steering Music Concepts[0] sits squarely within this cluster, sharing methodological DNA with Interpretable Music Concepts[1] in its use of sparse coding to extract interpretable features from generative music systems. While Interpretable Music Concepts[1] emphasizes discovering what concepts emerge naturally in trained models, Steering Music Concepts[0] appears to push further toward active manipulation and control of these discovered concepts. This contrasts with approaches like Audio Latent Features[3], which may focus more broadly on general audio rather than music-specific semantics, and with biomedical branches like Parkinsons Speech Biomarker[4] that apply similar sparse interpretability tools to entirely different domains. The central tension across these branches involves balancing the sparsity needed for human interpretability against the expressiveness required to capture complex generative behaviors.

Claimed Contributions

Unsupervised concept discovery pipeline for generative music models

The authors introduce a multi-stage pipeline that applies sparse autoencoders to transformer-based music generators (MusicGen) to extract interpretable features from residual stream activations without supervision. This is the first application of SAEs in the audio domain.

10 retrieved papers
Automated large-scale evaluation framework for discovered features

The authors develop an automated evaluation system that combines generative labeling using multimodal language models, classifier-based labeling with pretrained audio models, and CLAP-based semantic alignment to label and validate thousands of discovered features without manual annotation.

10 retrieved papers
Demonstration of feature steering for controllable generation

The authors show that discovered features can be used to steer model generation by adding scaled feature vectors to the residual stream during inference, establishing practical utility for controllable music generation beyond interpretability.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unsupervised concept discovery pipeline for generative music models

The authors introduce a multi-stage pipeline that applies sparse autoencoders to transformer-based music generators (MusicGen) to extract interpretable features from residual stream activations without supervision. This is the first application of SAEs in the audio domain.

Contribution

Automated large-scale evaluation framework for discovered features

The authors develop an automated evaluation system that combines generative labeling using multimodal language models, classifier-based labeling with pretrained audio models, and CLAP-based semantic alignment to label and validate thousands of discovered features without manual annotation.

Contribution

Demonstration of feature steering for controllable generation

The authors show that discovered features can be used to steer model generation by adding scaled feature vectors to the residual stream during inference, establishing practical utility for controllable music generation beyond interpretability.

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