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The paper introduces Continuous Audio Thinking (CoAT), a framework that equips large audio language models with a continuous latent workspace to organize acoustic information before generating textual responses, improving performance on audio reasoning, understanding, and transcription tasks without additional decoding cost.
MusTBench is a benchmark for evaluating temporal grounding in Large Audio-Language Models (LALMs) for music understanding. The authors propose MusT, a four-stage training recipe that significantly improves temporal grounding performance over existing models.
New research shows that imperceptible audio signals can hijack large audio-language models (LALMs) with 79-96% success, forcing them to execute unauthorized commands like web searches or sending emails. The technique, dubbed AudioHijack, targets generative models and works regardless of user input, posing a serious security risk to voice AI systems.
A comprehensive survey reviewing the trustworthiness challenges of Large Audio Language Models (LALMs), including vulnerabilities like cross-modal jailbreaking and acoustic backdoors, and proposing a defense-in-depth roadmap.