Challenge: Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge.
Approach: They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors .
Outcome: The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors .

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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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Challenge: Existing music-focused benchmarks are fragmented, largely single-modality, Western-centric . existing methods for evaluating MLLMs are lacking reproducibility and reliability .
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Challenge: Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples.
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Probing Audio-Visual Reasoning in Multimodal Language Models through the Lens of Audio (2026.acl-long)

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Challenge: Recent multimodal large language models lack robust audio-visual integration ability and performance on DeafTest is highly correlated with AV-Odyssey accuracy.
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Challenge: Existing AVQA methods often fail to link sound-producing objects in the video with the audio-visual information.
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Challenge: Existing benchmarks for testing audio modality of multimodal large language models focus on testing audio tasks in isolation.
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VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)

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Challenge: Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent.
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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering (2025.findings-emnlp)

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Challenge: Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications . despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets.
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Challenge: Recent advances in music large language models have significantly improved music understanding tasks, but the potential of incorporating additional modalities such as images, videos and textual music features remains unexplored.
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