StreamPro-Duplex: A Full-Duplex Multimodal Agent Framework for Always-On Streaming Interaction

Always-on streaming interaction requires assistants to continuously participate in open-ended interactions and to determine not only when to engage and what to say, but also how to interact with the user and which intent to serve as user multiple needs emerge, coexist, and evolve over time. Existing streaming systems have advanced the first two capabilities but remain limited in the latter two, largely following an Isolated Interaction paradigm that segments interaction into separate turns and handles user intents independently. Such isolation hinders natural full-duplex communication and makes it difficult to track multiple ongoing user needs across interleaved interactions. To address these limitations, we propose StreamPro-Duplex, a full-duplex multimodal agent framework for always-on streaming interaction. For how to interact, we decouple streaming understanding from interaction control and train a plug-and-play Duplex Controller that continuously determines and executes appropriate full-duplex actions. For which intent to serve, we design a persistent intent management mechanism that maintains evolving intents across interactions and coordinates concurrently triggered intents according to their priorities. We further train a unified Intent Monitor that jointly monitors multiple proactive intents over the shared streaming context in a single inference, avoiding repeated model execution for each intent. To support training, we construct StreamInteract-138K, a large-scale dataset covering representative always-on streaming interaction scenarios. Experiments demonstrate that StreamPro-Duplex achieves strong performance across full-duplex interaction, real-time streaming, and proactive streaming tasks, validating its effectiveness for always-on streaming interaction.