RIME: Enabling Large-Scale Agentic Music Post-Production

We formalize the task of agentic post-production, wherein individual aspects of a song are targeted, refined, and combined into a final track. Despite the promise of music generation models for one-shot output, such fine-grained iterative refinement workflows are a complementary problem, and largely out of their reach. We argue the bottleneck is data: existing corpora do not reflect how realistic post-production chains map onto the vocabulary musicians and engineers actually use. We introduce the Rule-based Instructions for Music Editing (RIME) framework, which generates realistic paired edit-instruction data from any baseline music dataset grounded in canonical methods, design patterns, and constraints derived from real production workflows. RIME leverages POEMS, a new toolkit that combines stem separation, mixing, and common studio effects for use by multimodal agents. We use POEMS and RIME to generate 3,000 pairs of edit instructions and ground truth audio, and use this data to evaluate existing multimodal LLMs as agents on this task, showing persistent challenges in current models' post-production capabilities.