AdaLoop lets audio language models spend reasoning depth by question difficulty, lifting average accuracy 2.9 to 3.8 points on three backbones
AdaLoop replaces the linear projection between an audio encoder and a language model with a lightweight recurrent module in which a shared, question-guided transformer block iteratively refines the audio representation and a learned halting mechanism decides how many steps each audio–question pair needs; across Qwen2.5-Omni-7B, Kimi-Audio-7B-Instruct, and MiMo-Audio-7B-Instruct on MMSU, MMAU-Pro, and MMAR it raises average accuracy by 2.9 to 3.8 points, with gains concentrated on perception-heavy subtasks, and the learned depth distribution shows signal-level questions averaging 5.7 iterations versus 2.6 for semantic ones.