DuplexSLA: A Full-Duplex Spoken Language Model with Synchronized Speech, Language, and Action
Abstract
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. However, existing duplex backbones still lack a native channel for in-conversation planning and tool calling, leaving real-time agentic behaviour either tied to turn boundaries or relegated to an external cascade. We propose DuplexSLA, a native full-duplex Speech-Language-Action foundation model that decodes assistant audio together with a structured action stream on a shared 160 ms chunk timeline. DuplexSLA is built on a dual-stream three-channel formulation: a continuous user audio channel, a discrete assistant audio channel, and a rate-limited textual action channel, all decoded jointly by a single backbone, so that listening, speaking, planning, and tool calling unfold on one shared clock. Two capabilities define the model: (1) semantic-driven turn-taking control, where interruption, pause, and backchannel are handled inside the same backbone instead of by an external semantic VAD; and (2) in-conversation planning and tool calling, where planning text and structured tool calls are emitted on the action channel without halting assistant audio, so that multi-action and backchannel-triggered tool use are interleaved with ongoing speech. To evaluate these capabilities together, we further construct DuplexSLA-Bench, a duplex benchmark covering pause, interrupt, and backchannel turn-taking together with three styles of in-conversation tool calling. Our project page, interactive demos, and the DuplexSLA-Bench evaluation suite are publicly available at https://github.com/hyzhang24/DuplexSLA.
Cite
@article{arxiv.2605.20755,
title = {DuplexSLA: A Full-Duplex Spoken Language Model with Synchronized Speech, Language, and Action},
author = {Haoyang Zhang and Jun Chen and Donghang Wu and Yuxin Li and Yuxin Zhang and Xiangyu Tony Zhang and Che Liu and Qingjian Lin and Yizhou Peng and Hexin Liu and Eng Siong Chng and Chao Yan and Boyong Wu and Yechang Huang and Xuerui Yang and Fei Tian},
journal= {arXiv preprint arXiv:2605.20755},
year = {2026}
}