# MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence

arXiv: https://arxiv.org/abs/2508.13992 · HTML: https://mlsanity.com/paper/2508.13992/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2025.

## Built on by (most cited first, 30 of 61)

- [Cognitive Foundations for Reasoning and Their Manifestation in LLMs](https://mlsanity.com/paper/2511.16660/) (2025)
- [Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction](https://mlsanity.com/paper/2512.14865/) (2025)
- [Fun-Audio-Chat Technical Report](https://mlsanity.com/paper/2512.20156/) (2025)
- [MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos](https://mlsanity.com/paper/2603.14145/) (2026)
- [STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence](https://mlsanity.com/paper/2510.24693/) (2025)
- [MOSS-Audio Technical Report](https://mlsanity.com/paper/2606.01802/) (2026)
- [VoxSafeBench: Not Just What Is Said, but Who, How, and Where](https://mlsanity.com/paper/2604.14548/) (2026)
- [All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation](https://mlsanity.com/paper/2604.24401/) (2026)
- [Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox](https://mlsanity.com/paper/2605.27772/) (2026)
- [AuTAgent: A Reinforcement Learning Framework for Tool-Augmented Audio Reasoning](https://mlsanity.com/paper/2602.13685/) (2026)
- [The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents](https://mlsanity.com/paper/2602.14224/) (2026)
- [ParaBridge: Bridging Paralinguistic Perception and Dialogue Behavior in Speech Language Models](https://mlsanity.com/paper/2606.10581/) (2026)
- [Hearing the Order: Investigating Position Bias in Large Audio-Language Models](https://mlsanity.com/paper/2510.00628/) (2025)
- [Robustness assessment of large audio language models in multiple-choice evaluation](https://mlsanity.com/paper/2510.04584/) (2025)
- [OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond](https://mlsanity.com/paper/2605.19660/) (2026)
- [VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents](https://mlsanity.com/paper/2510.11098/) (2025)
- [The MUSE Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMS](https://mlsanity.com/paper/2510.19055/) (2025)
- [Bagpiper: Solving Open-Ended Audio Tasks via Rich Captions](https://mlsanity.com/paper/2602.05220/) (2026)
- [Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization](https://mlsanity.com/paper/2604.06138/) (2026)
- [Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models](https://mlsanity.com/paper/2604.25591/) (2026)
- [Realtime-Venus: A full-duplex interaction system with asynchronous delegation](https://mlsanity.com/paper/2609.13814/) (2026)
- [SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models](https://mlsanity.com/paper/2601.07331/) (2026)
- [AudioRouter: Data Efficient Audio Understanding via RL based Dual Reasoning](https://mlsanity.com/paper/2602.10439/) (2026)
- [Escape the Language Prior: Mitigating Late-Stage Modality Collapse in Audio Reasoning via Modality-Aware Policy Optimization](https://mlsanity.com/paper/2605.27741/) (2026)
- [Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning](https://mlsanity.com/paper/2607.20166/) (2026)
- [CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challenges](https://mlsanity.com/paper/2509.22461/) (2025)
- [Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores](https://mlsanity.com/paper/2511.20697/) (2025)
- [ORCA: Open-ended Response Correctness Assessment for Audio Question Answering](https://mlsanity.com/paper/2512.09066/) (2025)
- [SARA: Stress Test Reasoning in Audio Deepfake Detection](https://mlsanity.com/paper/2601.03615/) (2026)
- [MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models](https://mlsanity.com/paper/2603.09714/) (2026)

## Abstract (arXiv)

Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challenging. To address this gap, we introduce MMAU-Pro, the most comprehensive and rigorously curated benchmark for assessing audio intelligence in AI systems. MMAU-Pro contains 5,305 instances, where each instance has one or more audios paired with human expert-generated question-answer pairs, spanning speech, sound, music, and their combinations. Unlike existing benchmarks, MMAU-Pro evaluates auditory intelligence across 49 unique skills and multiple complex dimensions, including long-form audio comprehension, spatial audio reasoning, multi-audio understanding, among others. All questions are meticulously designed to require deliberate multi-hop reasoning, including both multiple-choice and open-ended response formats. Importantly, audio data is sourced directly ``from the wild" rather than from existing datasets with known distributions. We evaluate 22 leading open-source and proprietary multimodal AI models, revealing significant limitations: even state-of-the-art models such as Gemini 2.5 Flash and Audio Flamingo 3 achieve only 59.2% and 51.7% accuracy, respectively, approaching random performance in multiple categories. Our extensive analysis highlights specific shortcomings and provides novel insights, offering actionable perspectives for the community to enhance future AI systems' progression toward audio general intelligence. The benchmark and code is available at https://sonalkum.github.io/mmau-pro.