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arXiv · 2509.12275

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering

Jinghua Zhao·Hang Su·Lichun Fan·Zhenbo Luo·Hui Wang·Haoqin Sun·Yong Qin
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Abstract

With the rapid progress of large audio-language models (LALMs), audio question answering (AQA) has emerged as a challenging task requiring both fine-grained audio understanding and complex reasoning. While current methods mainly rely on constructing new datasets via captioning or reasoning traces, existing high-quality AQA data remains underutilized. To address this, we propose Omni-CLST, an error-aware Curriculum Learning framework with guided Selective Chain-of-Thought. The framework efficiently leverages existing high-quality dataset through two key strategies: an error-aware curriculum that organizes samples by difficulty, and a guided thought dropout mechanism that focuses reasoning on challenging cases. Experiments show that Omni-CLST achieves 73.80% on MMAU-mini and a new state of the art of 64.30% on MMAR, demonstrating robust generalization in multimodal audio-language understanding.

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Jinghua Zhao, Hang Su, Lichun Fan, Zhenbo Luo, Hui Wang, Haoqin Sun, Yong Qin. 2025-09-14. Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering. https://arxiv.org/abs/2509.12275

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