The Muse Benchmark: Probing Music Perception and Auditory Relational Reasoning in Audio LLMs

Multimodal Large Language Models (MLLMs) have demonstrated capabilities in audio understanding, but current evaluations do not demonstrate relational reasoning. We introduce the Music Understanding and Structural Evaluation (MUSE) Benchmark, consisting of 10 tasks designed to probe fundamental music perception skills. We evaluate four SOTA models (Gemini Pro and Flash, Qwen2.5-Omni, and Audio-Flamingo 3) against humans (N=200). Results reveal a wide variance in SOTA capabilities and a persistent gap with human experts. While Gemini Pro succeeds on basic perception, Qwen and Audio Flamingo 3 perform at or near chance, exposing severe perceptual deficits. Furthermore, we find Chain-of-Thought (CoT) prompting provides inconsistent, often detrimental results. MUSE is a critical tool for evaluating invariant musical representations and driving development of more robust AI systems.