A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models
On the map: LLM reasoning → Audio-language model reasoning
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 34 references that are in the map. First posted on arXiv 27 Jan 2026.
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10 of 34 references.
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- 2025Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models10 Jul
- 2024MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark24 Oct
Other references · 8
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- 2020CoVoST 2 and Massively Multilingual Speech-to-Text Translationcited by 74
- 2024Salmon: A Suite for Acoustic Language Model Evaluationcited by 24
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Citing papers per year: 2026: 1.
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- 2025Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models
- 2025SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information
- 2025Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
- 2025Mellow: a small audio language model for reasoning
- 2026Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
- 2025MMAR: A Challenging Benchmark for Deep Reasoning in Speech, Audio, Music, and Their Mix
- 2025Investigating Faithfulness in Large Audio Language Models
- 2025See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models