Enhancing Audio Reasoning via Semantic Summary Prediction

Abstract:Large Audio Language Models (LALMs) perform well on complex question answering but often show a reasoning gap, where explicit Chain-of-Thought (CoT) reduces accuracy compared to direct answers. We hypothesize that long reasoning sequences shift attention away from the audio input. To address this, we propose SPARE (Semantic Prediction for Audio REasoning), which introduces a register token aligned with the final conclusion using a cosine similarity loss with a Sentence-BERT embedding. This conditions the model's latent space with the target semantic goal before reasoning begins. Experiments on MMAU and MMAR with SALMONN show improved zero-shot reasoning and stronger early attention to audio without additional inference cost.

Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)

arXiv:2609.20849 [cs.CL]

(or arXiv:2609.20849v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.20849 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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