RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios

While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics -- or Acoustic Ecology''-- that characterize authentic physical environments. To bridge this ecological gap, we introduce **RSA-Bench**, a comprehensive robustness benchmark designed to stress-test ALLMs through high-fidelity auditory scene simulations. Unlike traditional methods, we construct evaluation samples by naturally superimposing diverse environmental soundscapes -- spanning *Pasture*, *Extreme Weather*, *Classroom*, and *Outdoors* -- onto clean speech signals across a spectrum of interference intensities. By evaluating models on six core tasks ranging from fundamental perception to complex reasoning, our study unveils three macro-level insights: **(I) The Perception-Cognition Gap:** Models maintain relative resilience in low-level recognition but suffer a **functional collapse** in high-order reasoning tasks under stress; **(II) Scenario Sensitivity:** Vocal-like''interference (e.g., background laughter) proves significantly more destructive than mechanical noise, challenging the model's auditory attention mechanisms; and (III) The Denoising Paradox: Standard speech enhancement often exacerbates performance degradation, as ALLMs prove highly sensitive to the semantic distortions introduced by denoising artifacts.