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cs.SD2026

From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs

Yuanhe Zhang, Weiliu Wang, Jie Ren +7

Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency signals that are inaudible to…

cs.SD2026

A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook

Kaiwen Luo, Zhenhong Zhou, Leyan Wang +32

Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…

cs.SD2026

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

Yibo Zhang, Liang Lin, Kaiwen Luo +8

While Audio Large Models (ALMs) have achieved remarkable proficiency, their robustness remains brittle in real-world deployment. Existing evaluations largely rely on synthetic Gaus…

cs.SD2026

ChronosAudio: A Comprehensive Long-Audio Benchmark for Evaluating Audio-Large Language Models

Kaiwen Luo, Liang Lin, Yibo Zhang +8

Although Audio Large Language Models (ALLMs) have witnessed substantial advancements, their long audio understanding capabilities remain unexplored. A plethora of benchmarks have b…

cs.SD2025

Hidden in the Noise: Unveiling Backdoors in Audio LLMs Alignment through Latent Acoustic Pattern Triggers

Liang Lin, Miao Yu, Kaiwen Luo +9

As Audio Large Language Models (ALLMs) emerge as powerful tools for speech processing, their safety implications demand urgent attention. While considerable research has explored t…