Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in
  Spoken Language Models
Authors: name: Donghang Wu, name: Haoyang Zhang, name: Jun Chen, name: Xiangyu, name: Zhang, name: Hexin Liu, name: Eng Siong Chng, name: Fei Tian, name: Xuerui Yang, name: Xiangyu Zhang, name: Daxin Jiang, name: Gang Yu

Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought
(CoT) reasoning due to the prohibitive latency of generating the entire thought
process sequentially. Enabling SLMs to think while speaking, similar to humans,
is attracting increasing attention. We present, for the first time, Mind-Paced
Speaking (MPS), a brain-inspired framework that enables high-fidelity,
real-time reasoning. Similar to how humans utilize distinct brain regions for
thinking and responding, we propose a novel dual-brain approach, employing a
"Formulation Brain" for high-level reasoning to pace and guide a separate
"Articulation Brain" for fluent speech generation. This division of labor
eliminates mode-switching, preserving the integrity of the reasoning process.
Experiments show that MPS significantly outperforms existing
think-while-speaking methods and achieves reasoning performance comparable to
models that pre-compute the full CoT before speaking, while drastically
reducing latency. Under a zero-latency configuration, the proposed method
achieves an accuracy of 92.8% on the mathematical reasoning task Spoken-MQA and
attains a score of 82.5 on the speech conversation task URO-Bench. Our work
effectively bridges the gap between high-quality reasoning and real-time
interaction.