Learning When to Think While Listening in Large Audio-Language Models
Recent advances in Large Audio-Language Models (LALMs) have made real-time, streaming spoken interaction increasingly practical. In this setting, reasoning quality and responsiveness are tightly coupled: delaying reasoning until the speech endpoint can improve answer quality but moves deliberation into user-visible response delay, while answering too early risks committing before decisive evidence arrives. We introduce a learnable wait-think-answer control formulation for LALMs. Motivated by the incremental nature of human conversation, the controller decides under partial audio evidence when to wait, when to externalize a compact reasoning update, and when to answer. Using Qwen2.5-Omni-7...
| Authors | Zhiyuan Song, Weici Zhao, Yang Xiao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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