Papers with MMAU
Audio Query Handling System with Integrated Expert Models and Contextual Understanding (2025.emnlp-industry)
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| Challenge: | Existing chatbots are limited to specific audio tasks, but the domain of audio content related queries remains underexplored. |
| Approach: | They propose to use an intent classifier to route queries to audio-related experts using a diverse audio query dataset. |
| Outcome: | The proposed system outperforms state-of-the-art LLMs on custom audio tasks and MMAU sound set benchmarks. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)
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Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Simon Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang
| Challenge: | Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. |
| Approach: | They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks. |
| Outcome: | The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics. |
Temporal Contrastive Decoding: A Training-Free Method for Large Audio-Language Models (2026.findings-acl)
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| Challenge: | Large audio-language models (LALMs) can exhibit a temporal smoothing bias . unified decoders can produce less specific audio-grounded outputs . |
| Approach: | They propose a temporally blurred slow-path view that is re-encoded by a token-level logit update. |
| Outcome: | Experiments on MMAU and AIR-Bench show consistent improvements on strong unified LALMs. |
Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)
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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao
| Challenge: | Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception. |
| Approach: | They propose a framework that organizes audio information into three explicit components in a unified JSON format. |
| Outcome: | The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities. |
Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal reasoning overlook the audio modality. |
| Approach: | They propose a large-scale audio language model for deep reasoning that leverages a multitask audio dataset. |
| Outcome: | The proposed model performs well across key benchmarks including MMAU-mini, AIR-Bench chat/foundation, and MELD. |
Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding (2026.findings-acl)
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| Challenge: | Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors. |
| Approach: | They propose a large-scale dataset for **Perception-Aware Question Answering** that uses a hierarchical decoupling strategy to separate speech from environmental sounds and distinguishes among multiple speakers. |
| Outcome: | The proposed model improves on MMAU-mini, MMAR, and PAQA while maintaining comparable performance on multiple benchmarks. |