Year: 2026
Type: conference-paper
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 zeroshot reasoning and stronger early attention to audio without additional inference cost.
Source: Interspeech (USB)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7213891715