Challenge: Symbolic music understanding is useful for many music applications, but lack of training data hinders representation learning.
Approach: They propose a pre-trained model for music understanding that uses symbolic music data to train music representations.
Outcome: The proposed model improves on four music understanding tasks.

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Challenge: MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements.
Approach: a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests .
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Byte Pair Encoding for Symbolic Music (2023.emnlp-main)

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Challenge: Recent research has focused on reducing the overall sequence length by merging embeddings or combining tokens.
Approach: They propose to convert music into a sequence of discrete tokens using Byte Pair Encoding . they propose to combine tokens with embeddings to improve the results .
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Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music (2026.acl-long)

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Challenge: Existing symbolic music generation models represent musical notes as a sequence of attribute tokens with fixed unidirectional dependencies.
Approach: They propose a symbolic music generation framework that adopts a autoregressive and a discrete diffusion architectures for note attributes.
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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 .
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SpanBERT: Improving Pre-training by Representing and Predicting Spans (2020.tacl-1)

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Challenge: Pre-training methods like BERT mask individual words or subword units, but many tasks involve reasoning about relationships between two or more spans of text.
Approach: They propose a pre-training method that masks contiguous random spans instead of random tokens to train the span boundary representations to predict the entire content of the masked span.
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Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)

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Challenge: Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes.
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SciBERT: A Pretrained Language Model for Scientific Text (D19-1)

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Challenge: SciBERT is a pretrained language model based on BERT to improve performance on scientific NLP tasks.
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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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Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)

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Challenge: Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods.
Approach: This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods.
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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.
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