ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis

Automatic symbolic music analysis has made substantial progress, yet existing systems are typically designed for a single mode of use, such as full-score prediction, and therefore do not match the broader range of operations that arise in analysis workflows, including partial completion, local correction, and iterative refinement. As a result, there remains a gap between strong benchmark models and systems that can support interactive analytical use. We present a unified framework for symbolic Roman-numeral (RN) analysis that narrows this gap by combining strong predictive performance with direct support for constrained completion and revision. The method is designed to provide a practical trade-off between accuracy and interactive responsiveness by computing expensive pretrained representations once and reusing them during iterative refinement, making powerful pretrained models more amenable to interactive settings. It supports complete score analysis, targeted revision of existing labels, and inference of missing annotations from partial context through a shared modeling framework. Experiments on Dilemmadata, the largest and most heterogeneous benchmark of its kind, show that the proposed approach is a strong RN-analysis baseline while also supporting masked completion from partial labels. Together with a prototype interface for multi-level candidate inspection and editing, these results position automatic RN analysis not only as a prediction problem, but also as a foundation for future interactive tools for music analysis.

Audio-language models remain unreliable at extracting basic musical properties from audio, motivating pipelines that place a music information retrieval (MIR) tool between the audio and the model. The tool reports measurements such as keys, chords, and structural similarity, and the model is expected to derive the answer. We test whether it can. Holding the musical information fixed, we vary derivation depth: the number of reasoning steps between the tool output and the answer. When the report states the conclusion, all five models score near 100%, and they follow it even when it is wrong. One step away, accuracy drops. On modulation, open models fall to 57-81%, although they answer that single step correctly when asked directly, and reach 0% at greater depths. Frontier models degrade less, but still degrade. Adding the audio does not recover the loss. The bottleneck in tool-augmented music analysis is not access to musical information but reasoning from it, so tool design must account for how much derivation it leaves to the model.

Musicians commonly communicate musical intent through scores and performances. To investigate whether models can reason across both modalities, we introduce MuSP-Bench, a human-authored benchmark of 490 questions assessing understanding across Musical Scores and Performances, both individually and in combination. The benchmark spans interpretive and long-horizon reasoning across classical piano and orchestral works, represented through ABC notation, score images, MIDI, and audio. Our evaluation of frontier multimodal large language models reveals substantial difficulties in understanding musical scores and even greater challenges in reasoning about audio. The benchmark is available at https://musp.vaclis.net/.