Multi-Stream Architecture

A multi-stream architecture is a processing design in which separate concurrent data streams — typically audio, video, and text — are processed simultaneously rather than sequentially or through a single unified pipeline. In a conventional multimodal model, different modalities might be processed separately and fused at a specific stage, with one modality effectively blocking or waiting for another to complete. In a multi-stream architecture, each modality flows continuously and concurrently, with the model interleaving and synthesizing across all streams in real time.

The core motivation is that different modalities operate on fundamentally different timescales. Audio changes on the order of tens to hundreds of milliseconds; video frames arrive at 15-60fps depending on the source; text input is bursty and typically higher-bandwidth per token. A sequential or fused-late architecture would either delay processing of faster modalities to wait for slower ones, or lose information by down-sampling faster streams to match the rate of slower ones. Multi-stream processing preserves the native granularity of each modality and allows the model to reason across them without artificial synchronization delays.

Multi-stream architectures enable interaction modes that are impossible when modalities are processed in isolation or in sequence. If a user is speaking while pointing at something on screen, the model can respond to the visual context in the middle of a spoken sentence — just as a human collaborator might say "hold on, I see what you mean" in the middle of an explanation. This simultaneity of perception and response is what gives interaction models their qualitatively different feel compared to turn-based systems. The model does not need to wait for the user to finish a complete verbal turn before reacting to a visual event.

The challenge of multi-stream processing is maintaining coherence across streams that are advancing at different rates. The model must track which portions of each stream have been processed, integrate new information from any stream at any moment, and generate outputs that are contextually appropriate across all active modalities simultaneously. This requires careful state management and inference optimization — particularly the streaming-sessions approach of maintaining a persistent GPU sequence that accumulates chunks from all streams.