AdaLoop: Adaptive-Depth Latent Reasoning for Audio Language Models

Abstract:Large audio language models answer questions about speech, sound, and music, yet their accuracy drops sharply on tasks that need fine-grained acoustic analysis. Judging which of two speakers has the higher pitch demands iterative signal-level reasoning that a content question does not. Current models spend the same computational depth on both. We introduce AdaLoop, a lightweight recurrent module that learns how many latent refinement steps a given audio--question pair requires. A shared transformer block iterates over the audio representation, guided by the question, while a learned halting mechanism exits the loop once the representation is ready. AdaLoop adds fewer than 3\% of the base model's parameters and plugs into any audio encoder--language model pair without modifying either component. Evaluated on three architecturally distinct models across MMSU, MMAU-Pro, and MMAR, AdaLoop raises the average accuracy by 2.9 to 3.8 points, with the largest gains on perception-heavy subtasks where the model learns to apply deeper reasoning.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2610.06949 [cs.SD]

(or arXiv:2610.06949v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.06949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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