Realtime-Venus: A full-duplex interaction system with asynchronous delegation

Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and linguistic cues, while video interaction also requires grounding the conversation in evolving visual context. We present Realtime-Venus, a proactive full-duplex interaction system with two separately trained 9B models: Realtime-Venus-Omni for audio–visual interaction and Realtime-Venus-Audio for spoken interaction. Each model serves as a complete conversational frontend, integrating continuous perception, conversational control, and native speech generation through a shared causal timeline for user inputs, model outputs, and delegation events. A dual-loop runtime coordinates live interaction with background reasoning and tool execution. Foreground interaction continues while Realtime-Venus-Harness executes tasks asynchronously and returns results for integration into the ongoing dialogue. Both models follow a common post-training recipe combining offline understanding, proactive full-duplex trajectories, and delegation workflows. Among the evaluated online models, Realtime-Venus-Omni achieves the highest scores on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%).

Introduction. Natural interaction requires systems to interpret ongoing observations while deciding when and how to respond, including when to initiate a response without an explicit user request. Recent models increasingly integrate perception, speech generation, and conversational control. Moshi supports concurrent speech modeling (Défossez et al., 2024); Qwen2.5-Omni and Qwen3-Omni combine multimodal perception with native streaming speech generation (Xu et al., 2025a,b); and MiniCPM-o 4.5 extends these capabilities to proactive full-duplex video interaction (Cui et al., 2026). Research on spoken agents explores retrieval, tool calls, and asynchronous external computation during dialogue (Chien et al., 2026; Huang et al., 2026; OpenAI, 2026; Zhang et al., 2026). Continuous interaction and external computation operate on different timescales within the same session. A background task requires a stable record of the request and its supporting evidence, but its result must be interpreted in a conversation that may have changed during execution.

Discussion / Conclusion. Across the evaluated settings, Realtime-Venus demonstrates competitive multimodal understanding and strong conversational continuity under non-interruptive speech. Its asynchronous design provides access to external capabilities while keeping interaction active: background reasoning and tool execution proceed while the frontend continues receiving inputs and managing speech. Memory augmentation further supports hour-scale video understanding, with improvements across all evaluated duration bins. Together, these findings support coordinating immediate conversational responses with longer-running computation as a promising direction for assistants that remain engaged with users while handling tasks beyond the frontend’s own capabilities. Future work will explore finer-grained streaming chunks to better capture brief events and improve the timing of conversational responses.