Challenge: Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores.
Approach: They propose a benchmark for score-level musical understanding across textual and visual modalities.
Outcome: The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others.

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Thesis Proposal: Multimodal Benchmark for Music Understanding in Large Language Models (2026.eacl-srw)

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Challenge: Existing music-focused benchmarks are fragmented, largely single-modality, Western-centric . existing methods for evaluating MLLMs are lacking reproducibility and reliability .
Approach: They propose to develop a musically multimodal benchmark that will integrate music into the benchmark.
Outcome: The proposed benchmark will integrate culturally diverse musical material beyond the dominant Western canon.
The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models (2024.findings-acl)

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Challenge: Existing benchmarks to evaluate LLMs' capabilities are inadequate for assessing their musical capabilities.
Approach: They propose to use a large-scale music benchmark specifically designed to evaluate the music-related capabilities of large language models (LLMs).
Outcome: The proposed framework evaluates 16 large language models in the domain of music.
WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning (2025.emnlp-main)

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Challenge: Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks.
Approach: They propose a systematic taxonomy to evaluate MLLMs' ability to interpret real-world music scores and answer complex musicological queries.
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AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

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Challenge: Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach: They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
Outcome: The proposed framework evaluates the ability of large audio-language models to understand audio signals and interact with humans in the textual format.
TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)

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Challenge: Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities.
Approach: They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs.
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Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)

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Challenge: Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music.
Approach: They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics.
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AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)

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Challenge: Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases.
Approach: They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks .
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Beyond Facts- Benchmarking Distributional Reading Comprehension in Large Language Models (2026.findings-acl)

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Challenge: Existing reading comprehension benchmarks focus on factual information, but many real-world tasks require distributional knowledge expressed across text.
Approach: They propose a reading comprehension benchmark for LLMs to evaluate their ability to infer distributional knowledge from natural language.
Outcome: Experiments with multiple LLMs show that the model outperforms baselines, but performance varies widely across distribution types and characteristics.
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model (2025.findings-naacl)

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Challenge: Existing general-domain visual language models lack ability of music notation understanding . Symbolic music is represented in two distinct forms: auditory music and symbolic music .
Approach: They propose to train a multimodal music notation model using a large-scale dataset . they use cross-modal alignment to train the model for music notations analysis .
Outcome: The proposed model improves on music understanding by training with a multimodal music notation model.
BoYaEval: Evaluating Multimodal Large Language Models on Understanding Ancient Chinese Musical Scores (2026.acl-long)

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Challenge: Multimodal Large Language Models excel in general tasks but struggle with specialized, structured cultural symbols.
Approach: They evaluate 21 leading MLLMs and compare their performance to a benchmark for Ancient Chinese musical notation.
Outcome: The benchmark evaluates 21 leading MLLMs on five types of ancient Chinese music notation systems.

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