most citedChronological Thinking in Full-Duplex Spoken Dialogue Language Models

1 citations · 1 across the 4 of their papers we have counts for

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8 papers

eess.AS2026

Decoupling Conversational Dynamics in Full-Duplex Spoken Models through Reinforcement Learning

Yuxin Li, Donghang Wu, Guan-Ting Lin +4

Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, an…

cs.CL20261 cited

Chronological Thinking in Full-Duplex Spoken Dialogue Language Models

Donghang Wu, Haoyang Zhang, Chen Chen +8

Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user…

eess.AS2026

DuplexSLA: A Full-Duplex Spoken Language Model with Synchronized Speech, Language, and Action

Haoyang Zhang, Jun Chen, Donghang Wu +13

Recent advances in spoken dialogue language models have shifted from turn-based to full-duplex designs, where the model continuously listens to the user while generating responses.…

eess.AS2026

The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning

Donghang Wu, Tianyu Zhang, Yuxin Li +4

During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal cognitive processing may not always man…

cs.CL2026

Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models

Donghang Wu, Haoyang Zhang, Jun Chen +9

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.…

cs.CL2026

Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision

Shreyas Gopal, Donghang Wu, Ashutosh Anshul +5

Speech Large Language Models (LLMs) that understand and follow instructions in many languages are useful for real-world interaction, but are difficult to train with supervised fine…