Audio Reasoning

Audio reasoning asks models to answer questions and make decisions from speech, music, and environmental sound, including cues such as timing, source location, emotion, and overlapping events. Common approaches pair audio encoders with language models, then use structured chain-of-thought, source separation, acoustic tools, or reinforcement learning to connect answers to sound evidence. Benchmarks such as MMAU and MMAR test broad audio question answering, while PolyBench and long-audio and spatial-audio evaluations probe compositional relations, temporal grounding, and overlapping sources. A central challenge is keeping extended reasoning grounded in acoustics rather than language priors, while preserving accuracy across long recordings, accents, and noisy or polyphonic audio.