StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs

StreamTTT is a streaming vision-language model that balances real-time perception with long-term memory by writing long-range history into fast weights outside the attention context, while keeping a short sliding key-value cache for recent evidence. The model is trained on both offline long-video QA and a new real-time QA corpus, and it outperforms SimpleStream-4B on OVO-Bench by 1.4 points in real-time perception and 3.7 points in backward tracing. StreamTTT-4B also competes with the larger SimpleStream-8B on the StreamingBench Real-Time Visual Understanding subset.

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