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.

翻译：近年来，多模态大语言模型（MLLMs）在各种视觉-语言任务中展现出令人印象深刻的能力。然而，其在多模态符号音乐领域的推理能力在很大程度上仍未得到探索。我们提出了WildScore，这是首个面向真实场景的多模态符号音乐推理与分析评测基准，旨在评估MLLMs解读真实世界乐谱并回答复杂音乐学问题的能力。WildScore中的每个实例均源自真实的音乐作品，并附有真实的用户生成问题与讨论，从而捕捉了实际音乐分析的复杂性。为促进系统化评估，我们提出了一个系统化的分类体系，包含高层级与细粒度的音乐学本体。此外，我们将复杂的音乐推理任务构建为多项选择题问答形式，从而实现对MLLMs符号音乐理解能力的可控且可扩展的评估。在WildScore上对当前最先进的MLLMs进行的实证评测揭示了其在视觉-符号推理方面的有趣模式，既展现了MLLMs在符号音乐推理与分析方面的有前景的方向，也揭示了其持续存在的挑战。我们公开了数据集与代码。