  Controllable music generation plays a vital role in human-AI music
co-creation. While Large Language Models (LLMs) have shown promise in
generating high-quality music, their focus on autoregressive generation limits
their utility in music editing tasks. To address this gap, we propose a novel
approach leveraging a parameter-efficient heterogeneous adapter combined with a
masking training scheme. This approach enables autoregressive language models
to seamlessly address music inpainting tasks. Additionally, our method
integrates frame-level content-based controls, facilitating track-conditioned
music refinement and score-conditioned music arrangement. We apply this method
to fine-tune MusicGen, a leading autoregressive music generation model. Our
experiments demonstrate promising results across multiple music editing tasks,
offering more flexible controls for future AI-driven music editing tools. The
source codes and a demo page showcasing our work are available at
https://kikyo-16.github.io/AIR.
