Audio Reasoning for AGI: First Comprehensive Survey of Multimodal Large Models and Four Frontier Paths

Problem Statement

Current multimodal models can transcribe audio but often discard acoustic cues such as tone, emotion, spatial context, and speaker dynamics, preventing genuine audio‑grounded reasoning.

Definition of Audio Reasoning

Audio reasoning is defined as inference that is explicitly anchored in continuous, fine‑grained acoustic evidence rather than relying on a textual transcription of the sound.

Unified Taxonomy

Audio‑to‑Text Reasoning – Directly extracts logical chains from raw audio without intermediate transcripts. Implementations include inference‑time chain‑of‑thought (CoT), instruction‑tuned (SFT) CoT, and reinforcement‑learning (RL)‑based CoT. The survey notes that CoT can mislead models on difficult tasks and that some audio‑question answering can be solved using only textual cues, highlighting the need for acoustic grounding. <think> Audio‑to‑Speech Reasoning – End‑to‑end systems that generate spoken responses while preserving the input’s prosody, emotion, and other paralinguistic features. Two real‑time paradigms are discussed: “Thinking While Listening” (simultaneous inference during user speech) and “Thinking While Speaking” (pre‑computing future reasoning using the playback time of generated audio) to balance depth of reasoning with latency.

Audio‑Visual Reasoning – Joint inference over synchronized audio and visual streams. Challenges addressed include temporal alignment, speaker identification, multimodal disambiguation, and the need for models to align continuous signals on the time axis rather than simply concatenating transcribed text with visual embeddings.

Agentic Audio Reasoning – Extends audio reasoning to autonomous agents that perceive, plan, and act based on auditory inputs. Two workflow categories are identified: (1) predefined workflow agents that follow a fixed perception‑planning‑action pipeline, and (2) dynamic tool‑calling agents where an LLM planner selects utilities such as ASR, TTS, web search, email, or calendar APIs on‑the‑fly.

Data Sources and Benchmark Practices

Existing large‑scale audio datasets include MMAU and VoxEval . Some works generate synthetic QA pairs and reasoning chains using large language models (e.g., LLM‑ALM) and augment them with acoustic features like speech rate, pitch, and stress to reduce text‑only shortcuts. The survey stresses that evaluation must go beyond answer accuracy; benchmarks should verify that models truly use audio evidence. Proposed future benchmark dimensions are tone, emotion, environmental sounds, speaker identity, long‑form context (e.g., podcasts, meetings), and audio‑video grounding.

Key Challenges and Future Research Directions

Reliability of synthetic audio‑reasoning data and mitigation of modality hallucination.

Detection and reduction of text‑surrogate shortcuts that allow models to answer without acoustic grounding.

Achieving low‑latency, real‑time interaction while maintaining deep acoustic reasoning.

Handling long‑duration audio contexts and maintaining coherence across extended sequences.

Shifting audio‑reasoning capabilities from post‑training fine‑tuning to earlier stages such as pre‑training or mid‑training (e.g., integrating audio reasoning objectives into the base model).

Conclusion

The survey establishes audio reasoning as a distinct research area, provides a comprehensive taxonomy, analyzes model architectures, training strategies, and evaluation metrics, and outlines concrete avenues for advancing acoustic‑grounded AI toward truly understanding and reasoning about sound.