WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
Authors: name: Gagan Mundada, name: Yash Vishe, name: Amit Namburi, name: Xin Xu, name: Zachary Novack, name: Julian McAuley, name: Junda Wu

Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated
impressive capabilities across various vision-language tasks. However, their
reasoning abilities in the multimodal symbolic music domain remain largely
unexplored. We introduce WildScore, the first in-the-wild multimodal symbolic
music reasoning and analysis benchmark, designed to evaluate MLLMs' capacity to
interpret real-world music scores and answer complex musicological queries.
Each instance in WildScore is sourced from genuine musical compositions and
accompanied by authentic user-generated questions and discussions, capturing
the intricacies of practical music analysis. To facilitate systematic
evaluation, we propose a systematic taxonomy, comprising both high-level and
fine-grained musicological ontologies. Furthermore, we frame complex music
reasoning as multiple-choice question answering, enabling controlled and
scalable assessment of MLLMs' symbolic music understanding. Empirical
benchmarking of state-of-the-art MLLMs on WildScore reveals intriguing patterns
in their visual-symbolic reasoning, uncovering both promising directions and
persistent challenges for MLLMs in symbolic music reasoning and analysis. We
release the dataset and code.