Our research enables AI systems that break free from traditional turn-taking limitations. We develop architectures capable of processing multiple concurrent audio, video, and data streams in real-time, with full-duplex communication and dynamic stream prioritization.
Our streaming architecture processes inputs as they arrive, without waiting for complete utterances or frames. This enables truly interactive systems that can respond while simultaneously receiving new inputs.
The system maintains separate attention contexts for each stream while efficiently sharing computation across concurrent inputs, achieving real-time performance even with multiple active streams.
We have developed novel mechanisms for integrating asynchronous API responses and database queries into the real-time inference pipeline. The model can issue queries, continue processing other streams, and seamlessly incorporate returned results.
This capability enables sophisticated agent behaviors such as real-time fact-checking, knowledge retrieval, and external tool use without interrupting the conversation flow.
Our systems intelligently prioritize attention across multiple concurrent streams based on context, urgency, and relevance. This adaptive allocation ensures responsive interactions even under heavy computational load.
The prioritization mechanism learns from interaction patterns and can be configured for different application requirements.
Unlike traditional chatbot architectures, our models support true full-duplex communication where the system can speak while listening, similar to human conversation. This enables natural interruptions, backchanneling, and overlapping speech.