A Survey of Audio Reasoning in Multimodal Foundation Models
Abstract
Reasoning has become a defining capability of modern foundation models, yet its development in the audio modality remains limited. Audio poses challenges that are distinct from those of text and vision. It is continuous, temporally dense, and contains linguistic, paralinguistic, and environmental information at multiple time scales. As a result, audio reasoning models must align acoustic signals with the discrete semantic space of large language models, while still preserving fine-grained information needed for reliable inference. Progress is also limited by three major obstacles: the scarcity of genuinely audio-grounded reasoning data, shortcut learning and modality hallucination, and the tension between reasoning depth and real-time latency in spoken interaction. In this paper, we present the first dedicated survey of audio reasoning. We provide a unified formulation that distinguishes direct predictive modeling from reasoning-augmented generation, review the architectural and training foundations of audio reasoning models, and systematically organize recent advances in Audio-to-Text, Audio-to-Speech, Audio-Visual Reasoning and Agentic Audio Reasoning. We further examine emerging paradigms such as Chain-of-Thought prompting, supervised fine-tuning, reinforcement learning, and latency-aware spoken interaction, and discuss evaluation practices, open challenges, and future directions. Our goal is to offer a coherent roadmap for developing robust, efficient, and natively grounded audio reasoning systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is its comprehensive survey of audio reasoning in multimodal foundation models, providing a structured framework for understanding the unique challenges and opportunities in this emerging field. The detailed taxonomy and critical review of existing methodologies lay the groundwork for future advancements in audio reasoning systems, although the lack of original empirical contributions limits its immediate impact.
Methodology
The paper presents a comprehensive survey of audio reasoning in multimodal foundation models, establishing a unified formulation and taxonomy that distinguishes various reasoning paradigms. It critically reviews model architectures and training methods, emphasizing the unique challenges posed by audio reasoning compared to text and vision. The authors effectively categorize existing works and identify gaps in the literature, providing a structured approach to understanding the state of the field. However, the survey lacks experimental validation or novel empirical results, which could have strengthened its contributions.
Results & evaluation
The paper does not include original experiments or datasets, focusing instead on reviewing existing literature and methodologies. While it provides a thorough overview of current techniques and challenges, the lack of empirical results limits the assessment of the proposed frameworks. The discussion on evaluation practices is insightful but would benefit from concrete examples or case studies.
Reproducibility
As a survey paper, it does not present original experiments or code implementations, which limits reproducibility. However, the comprehensive review of existing methods and datasets allows researchers to build upon the findings. The paper could enhance reproducibility by providing clearer references to datasets and methodologies discussed.
Limitations
The primary limitation is the absence of original experimental results or benchmarks, which would have provided empirical evidence for the claims made. Additionally, while the survey identifies challenges in audio reasoning, it does not propose concrete solutions or methodologies to address these issues.
Broader impact
The paper has significant implications for the development of audio reasoning systems, which are crucial for advancing human-computer interaction and AI applications in real-world scenarios. By highlighting the unique challenges and opportunities in audio reasoning, it sets the stage for future research that could lead to more robust and efficient multimodal systems. The main contribution of this paper is its comprehensive survey of audio reasoning in multimodal foundation models, providing a structured framework for understanding the unique challenges and opportunities in this emerging field. The detailed taxonomy and critical review of existing methodologies lay the groundwork for future advancements in audio reasoning systems, although the lack of original empirical contributions limits its immediate impact.
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