SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty

Yixuan Li; Minghao Lv; Xuenan Xu; Zihao Zheng; Mengyue Wu

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arXiv · 2610.13081

SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty

Yixuan Li·Minghao Lv·Xuenan Xu·Zihao Zheng·Mengyue Wu

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Abstract

Recent work on large audio-language models (LALMs) explores adaptive reasoning to balance accuracy and efficiency. However, existing approaches often rely on coarse-grained difficulty estimation, overlooking the diverse factors that determine reasoning effort. We address this limitation by deriving five-dimensional difficulty scores and compressed reasoning traces from complementary offline assessments. Based on this characterization, we propose SWEAR, a difficulty-aware reasoning framework that learns difficulty signals during supervised fine-tuning and uses reinforcement learning to adapt reasoning allocation across reasoning stages. Experiments on MMAU-test-mini and MMAR show that SWEAR improves accuracy while substantially reducing generated tokens, shortening reasoning for easy instances without sacrificing performance on difficult ones.

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Yixuan Li, Minghao Lv, Xuenan Xu, Zihao Zheng, Mengyue Wu. 2026-10-09. SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty. https://arxiv.org/abs/2610.13081

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