The paper introduces SWEAR, a framework for audio-language models that uses a five‑dimensional difficulty score to guide reasoning. By learning difficulty signals during fine‑tuning and applying reinforcement learning, SWEAR allocates reasoning effort adaptively across stages. Experiments on MMAU‑test‑mini and MMAR demonstrate that SWEAR boosts accuracy while cutting the number of generated tokens, especially for easier cases.
By Yixuan Li, Minghao Lv, Xuenan Xu, Zihao Zheng, Mengyue Wu
arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.
By Hui Geng, Yi Su, Han Yin, Tianjiao Wan, Qisheng Xu, Jiaxin Chen, Zijian Gao, Hengzhu Liu, Xie Chen, Kele Xu
arXiv:2609.23589v1 Announce Type: cross
Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...
By Jiaheng Dong, Xiaofeng Yu, Jean Honorio, Abhirup Ghosh, Hong Jia, Ting Dang
arXiv:2603.02266v2 Announce Type: replace-cross
Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-La...
By Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Aokai Hao, Haishu Zhao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Qiaozhi He, Tong Xiao, Jingbo Zhu
arXiv:2512. 09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art.
By \v{S}imon Sedl\'a\v{c}ek, Sara Barahona, Bolaji Yusuf, Laura Herrera-Alarc\'on, Santosh Kesiraju, Cecilia Bola\~nos, Alicia Lozano-Diez, Sathvik Udupa, Fernando L\'opez, Allison Ferner, Ramani Duraiswami, Jan \v{C}ernock\'y
Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e. g.
arXiv:2609.20849v1 Announce Type: new
Abstract: Large Audio Language Models (LALMs) perform well on complex question answering but often show a reasoning gap, where explicit Chain-of-Thought (CoT) re...
By Francesco Bonzi, Pooneh Mousavi, Cem Subakan, Mirco Ravanelli
arXiv:2607. 20166v1 Announce Type: cross Abstract: Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.
By Siqian Tong, Xuan Li, Chaozhuo Li, Baolong Bi, Yiwei Wang, Yujun Cai, Shenghua Liu, Chengpeng Hao
AdaLoop is a lightweight recurrent module that adaptively determines how many latent refinement steps are needed for audio–question pairs, allowing deeper reasoning only when necessary. It shares a transformer block that iterates over the audio representation guided by the question, with a learned halting mechanism that exits the loop once the representation is ready. Adding fewer than 3 % of the base model’s parameters, AdaLoop improves average accuracy by 2.9 to 3.8 points across three distinct models, especially on perception-heavy subtasks.
By Lee Seung-woo, Bowen Qi
arXiv:2609.27389v1 Announce Type: cross
Abstract: Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is c...
By Yuxiang Wang, Shengbo Cai, Yingda Shen, Ming-Hao Hsu, Qinke Ni, Liqiang Zhang, Teddy Sun, Steve Yevs, Zhizheng Wu
arXiv:2606. 03116v1 Announce Type: cross Abstract: The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust alignment evaluation.
By Haitao Li, Tian Tan, Yuguang Yang, Shan Yang, Xie Chen
AURAL is a speech language model that performs adaptive latent reasoning by modeling multiple plausible reasoning continuations in latent space and jointly predicting chunks of future states, thereby reducing sequential forward passes and latency. The authors introduce a large bilingual dataset, AuralReason-683K, containing concise chain‑of‑thought annotations for emotion recognition, empathetic dialogue, and general reasoning, and use reinforcement learning (AURAL‑RL) to reward concise, high‑quality reasoning that adapts to problem difficulty. Experiments on two backbones show that AURAL‑RL matches or exceeds chain‑of‑thought reinforcement learning while achieving significant latency reductions, such as an 11.8× speed‑up on Qwen2.5‑Omni.
"whyItMatters":"The work demonstrates that latent reasoning can match the performance of explicit chain‑of‑thought methods while dramatically cutting response time, addressing the trade‑off between intelligence and speed in speech language models."
By Yuxiang Wang, Kunyu Feng, Yuancheng Wang, Zihang Liu, Shengbo Cai, Qinke Ni, Wan Lin, Tao Feng, Yingda shen, Ming-Hao Hsu, Zhixian Zhao, Liqiang Zhang, Teddy Sun, Steve Yves, Zhizheng Wu