WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
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.
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Gagan Mundada
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Yash Vishe
68be3e13c123124955ef5f9b
Amit Namburi
68be3e13c123124955ef5f9c
Xin Xu
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2025-09-08T07:01:58.002Z
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Xin Xu
user
XinXuNLPer
68be3e13c123124955ef5f9d
Zachary Novack
claimed_verified
2025-09-08T07:02:00.032Z
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Zachary Novack
user
ZacharyNovack
68be3e13c123124955ef5f9e
Julian McAuley
68be3e13c123124955ef5f9f
Junda Wu
WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
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.
WildScore evaluates MLLMs' symbolic music reasoning through a benchmark of real-world music scores and user-generated queries, revealing both strengths and challenges.
Multimodal Large Language Models MLLMs multimodal symbolic music reasoning WildScore musicological queries musicological ontologies multiple-choice question answering visual-symbolic reasoning