标题： WildScore：在真实场景中的符号音乐推理基准测试 显示英文标题

标题： WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning

摘要： 最近在多模态大语言模型（MLLMs）方面的进展已在各种视觉-语言任务中展示了令人印象深刻的能力。 然而，它们在多模态符号音乐领域的推理能力仍大多未被探索。 我们引入了WildScore，这是首个在真实场景中的多模态符号音乐推理与分析基准，旨在评估MLLMs解释现实世界音乐谱和回答复杂音乐学问题的能力。 WildScore中的每个实例都来源于真实的音乐作品，并配有真实用户生成的问题和讨论，捕捉了实际音乐分析的复杂性。 为了便于系统评估，我们提出了一种系统分类法，包括高层次和细粒度的音乐学本体论。 此外，我们将复杂的音乐推理框架化为多项选择题回答，从而实现对MLLMs符号音乐理解的受控和可扩展评估。 在WildScore上对最先进MLLMs的实证基准测试揭示了其视觉-符号推理中的有趣模式，揭示了MLLMs在符号音乐推理与分析中的有前景方向和持续挑战。 我们发布了数据集和代码。 显示英文摘要

摘要： 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.