New survey maps audio reasoning challenges for multimodal AI models

Reasoning has become a hallmark of modern foundation models, but audio has lagged behind text and vision. Audio is continuous, temporally dense, and carries linguistic, paralinguistic, and environmental information across multiple time scales. This makes it uniquely challenging to align acoustic signals with the discrete semantic space of large language models while preserving fine-grained detail for reliable inference. A new survey from Zhihan Guo, Wenqian Cui, Guan-Ting Lin, and nine other authors provides the first comprehensive overview of audio reasoning in multimodal foundation models. The paper systematically reviews architectural foundations—covering Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic reasoning paradigms—and offers a unified formulation that distinguishes direct predictive modeling from reasoning-augmented generation.

The survey identifies three critical obstacles: the scarcity of genuinely audio-grounded reasoning data, pervasive shortcut learning and modality hallucination, and the inherent tradeoff between reasoning depth and real-time latency in spoken interactions. To address these, the authors explore emerging training strategies such as Chain-of-Thought prompting, supervised fine-tuning, reinforcement learning, and latency-aware spoken interaction designs. They also discuss evaluation practices and open challenges. By providing a coherent roadmap, this work aims to accelerate development of robust, efficient, and natively grounded audio reasoning systems—a key step toward truly multimodal AI that understands not just words, but tone, context, and environment.