MUGEN Benchmark Reveals Multi-Audio Weaknesses in Large Audio-Language Models

Jul 14, 2026

Researchers have introduced MUGEN, a benchmark designed to evaluate the ability of large audio-language models (LALMs) to understand multiple simultaneous audio inputs across speech, general audio, and music. Experiments show that LALMs experience consistent performance degradation as the number of concurrent audio inputs increases, highlighting input scaling as a key bottleneck. Training-free strategies such as Audio-Permutational Self-Consistency improve accuracy by up to 6.28%, and combining this with Chain-of-Thought reasoning further boosts performance to 6.74%.

Why it matters: This work exposes critical limitations in current LALMs' ability to process multiple audio streams, which is essential for real-world applications like meeting transcription and sound scene analysis.

Full story at: arXiv Audio and Speech Processing ↗