Fangda Ye, Kuicai Dong, Xie Zhifei, Yuxin Hu, Yihang Yin, Shurui Huang, Shikai Dong, Chen Zhang, Jianzhu Bao, Shuicheng Yan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recent advancements in multimodal reasoning overlook the audio modality.We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning.We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations.Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process.These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA.Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning.Experiments show state-ofthe-art performance across key benchmarks, including MMAU-mini (+25.42%),AIR-Bench chat/foundation (+14.57%/+10.13%),and MELD (+8.01%).Our findings stress the core of structured CoT training in advancing audio reasoning.

We present Pask, a proactive AI agent that provides real-time, context-aware guidance and knowledge support in audio-centric media environments. Unlike passive assistants that follow the ''you ask, I answer'' model, Pask shifts toward ''answering before asking'' by continuously monitoring live audio, anticipating user needs, and proactively offering conceptual explanations and semantic clarifications. It integrates three core components: a silent copilot for in-situ explanation, a structured knowledge base for factual grounding, and a private memory module for personalized adaptation. Pask enhances comprehension and communication in scenarios such as online learning, media consumption, and live meetings through sustained, intelligent guidance. A live demo is available at https://www.youtube.com/watch?v=ki_CKiV9Oyk.