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New Benchmark MMAU-Pro Pushes AI's Audio Understanding to New Limits

Scientists have developed MMAU-Pro, a comprehensive benchmark designed to rigorously assess the audio intelligence of artificial intelligence systems. The benchmark, featuring over 5,300 carefully curated instances, covers a wide spectrum of audio comprehension, from spoken language and environmental sounds to music, and their complex combinations. Early evaluations reveal that even the most advanced AI models exhibit significant limitations, struggling with nuanced audio tasks.

Understanding the world through sound is a fundamental aspect of human intelligence. As AI systems become increasingly sophisticated and integrated into our lives, it’s crucial they possess comparable auditory capabilities. However, evaluating this “audio general intelligence” has been a significant challenge, with existing benchmarks often falling short of capturing the complexity of real-world audio scenarios.

To address this gap, researchers have introduced MMAU-Pro. This new benchmark goes beyond previous efforts by encompassing a vast array of audio types and, crucially, by testing AI models on their ability to perform complex, multi-step reasoning across these sounds. MMAU-Pro includes instances that require understanding long-form audio (up to 10 minutes), deciphering spatial audio cues, analyzing multiple audio streams simultaneously, comprehending multicultural music, and following specific instructions.

For instance, MMAU-Pro might present an AI with a soundscape of a busy street and ask it to identify not just the prominent sounds like car horns or human voices, but also to infer the location of a specific event, such as a dropped object, based on subtle auditory clues like echoes or the type of surface it landed on. Another example could involve a piece of music from a non-Western tradition, requiring the AI to identify its cultural origin and specific instruments used, going beyond simple genre classification.

The benchmark is built upon 5,305 expertly annotated question-answer pairs, covering 49 distinct audio skills. Importantly, the audio data is sourced directly from real-world recordings, avoiding potential biases introduced by existing, often limited, datasets.

The initial evaluation of 22 leading AI models, including prominent names like Gemini 2.5 Flash and Audio Flamingo 3, yielded concerning results. Even the top-performing models struggled significantly, with accuracy scores in some categories dipping to around 50%, barely above random chance. The researchers found that models often exhibited shallow audio grounding and degraded performance when tasks moved from simple recognition to complex reasoning, particularly in areas like multi-audio analysis, spatial reasoning, and understanding less common musical traditions.

“Our evaluation across open and proprietary LALMs demonstrates that even the strongest models struggle across several categories,” the paper states. “These results underscore the importance of questions with minimal language priors to more faithfully evaluate audio-language understanding.”

The development of MMAU-Pro represents a significant step forward in the quest for artificial general intelligence. By providing a more comprehensive and challenging assessment of auditory capabilities, it aims to guide future research and accelerate the development of AI systems that can truly understand and interact with the world through sound. The benchmark and its associated code are publicly available, encouraging wider community participation in advancing this critical area of AI research.