MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic
  Evaluation of Audio General Intelligence
Authors: name: Sonal Kumar, name: Šimon Sedláček, name: Vaibhavi Lokegaonkar, name: Fernando López, name: Wenyi Yu, name: Nishit Anand, name: Hyeonggon Ryu, name: Lichang Chen, name: Maxim Plička, name: Miroslav Hlaváček, name: William Fineas Ellingwood, name: Sathvik Udupa, name: Siyuan Hou, name: Allison Ferner, name: Sara Barahona, name: Cecilia Bolaños, name: Satish Rahi, name: Laura Herrera-Alarcón, name: Satvik Dixit, name: Siddhi Patil, name: Soham Deshmukh, name: Lasha Koroshinadze, name: Yao Liu, name: Leibny Paola Garcia Perera, name: Eleni Zanou, name: Themos Stafylakis, name: Joon Son Chung, name: David Harwath, name: Chao Zhang, name: Dinesh Manocha, name: Alicia Lozano-Diez, name: Santosh Kesiraju, name: Sreyan Ghosh, name: Ramani Duraiswami

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.