๐ค AI Summary
Existing models struggle to simultaneously support real-time interaction, complex reasoning, and tool use within a full-duplex multimodal framework. This work proposes an asynchronous full-duplex architecture that decouples interaction from reasoning: an interaction layer processes audio and video inputs in an end-to-end streaming fashion to generate immediate responses, while a thinking layer employs plug-in modules to perform asynchronous, complex reasoning and tool invocation. To facilitate training, the authors introduce a Writer-Director pipeline for constructing continuous interactive data. Evaluated on multiple public benchmarks, the system demonstrates strong performance, significantly enhancing the naturalness and fluency of multimodal interactions.
๐ Abstract
Human interaction is continuous, multimodal, and full-duplex by nature. Although recent omni models have made substantial progress in unified speech, vision, and text modeling, combining seamless real-time interaction with complex reasoning and tool use remains challenging. We present DuplexOmni, a method for real-time multimodal full-duplex interaction. DuplexOmni separates model capability into an interaction layer and a thinking layer, which collaborate asynchronously in parallel. The interaction layer is implemented by the DuplexOmni model, an end-to-end system that processes streaming audio and video inputs while generating text and speech responses in real time. The thinking layer is a pluggable module that provides complex reasoning and tool-use capabilities. To support this method, we further develop a Writer-Director pipeline for constructing continuous-interaction training data. Experiments show that DuplexOmni achieves strong performance on multiple public benchmarks and exhibits natural full-duplex interaction ability.