A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models

Fuente: arXiv
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Autori principali: Christop, Iwona, Czyżnikiewicz, Mateusz, Skórzewski, Paweł, Bondaruk, Łukasz, Kubiak, Jakub, Lewandowski, Marcin, Kubis, Marek
Natura: Preprint
Pubblicazione: 2026
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author Christop, Iwona
Czyżnikiewicz, Mateusz
Skórzewski, Paweł
Bondaruk, Łukasz
Kubiak, Jakub
Lewandowski, Marcin
Kubis, Marek
author_facet Christop, Iwona
Czyżnikiewicz, Mateusz
Skórzewski, Paweł
Bondaruk, Łukasz
Kubiak, Jakub
Lewandowski, Marcin
Kubis, Marek
contents The present benchmarks for testing the audio modality of multimodal large language models concentrate on testing various audio tasks such as speaker diarization or gender identification in isolation. Whether a multimodal model can answer the questions that require reasoning skills to combine audio tasks of different categories, cannot be verified with their use. To address this issue, we propose Audio Reasoning Tasks (ART), a new benchmark for assessing the ability of multimodal models to solve problems that require reasoning over audio signal.
format Preprint
id arxiv_https___arxiv_org_abs_2601_19673
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models
Christop, Iwona
Czyżnikiewicz, Mateusz
Skórzewski, Paweł
Bondaruk, Łukasz
Kubiak, Jakub
Lewandowski, Marcin
Kubis, Marek
Sound
Artificial Intelligence
I.2.7
The present benchmarks for testing the audio modality of multimodal large language models concentrate on testing various audio tasks such as speaker diarization or gender identification in isolation. Whether a multimodal model can answer the questions that require reasoning skills to combine audio tasks of different categories, cannot be verified with their use. To address this issue, we propose Audio Reasoning Tasks (ART), a new benchmark for assessing the ability of multimodal models to solve problems that require reasoning over audio signal.
title A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models
topic Sound
Artificial Intelligence
I.2.7
url https://arxiv.org/abs/2601.19673