The OpenMOSS team open-sourced MOSS-VL-Realtime on July 14 — an 11B-parameter real-time streaming video vision-language model, pushing VLM from a "load the whole video first, then answer" batch-processing paradigm to a "watch, wait, revise on the fly" real-time streaming paradigm. Three core innovations. First, interactive paradigm redefinition: users can insert questions at any moment, the model answers immediately based on the current frame; when visual evidence is insufficient or the scene has no key change, the model actively emits <|silence|> to remain silent; when a new frame arrives, previously given answers are immediately revised rather than locked into the initial interpretation. Behind this is a unified interleaved token stream — video frames, user questions, and model answers are spliced on the same timeline, with questions inserted like "danmaku" (bullet comments), and the model can be steered in a new visual direction mid-answer. Second, Decoupled Cross-Attention: decoupling the cross-attention between visual feature extraction and text generation significantly reduces end-to-end throughput and latency at high frame rates. Third, XRoPE (Cross-dimensional Rotary Positional Encoding): maps the spatial dimensions (h, w) and the temporal dimension t with a single set of rotary position encodings, so the model knows exactly "when, where, and what happened" within a 256K long context, maintaining spatiotemporal consistency even when slices are truncated. Compared to the same period's Vidu S1, Wan-Streamer, NVIDIA Cosmos 3 and other real-time models that lean toward "video generation", MOSS-VL-Realtime directly targets real-time video understanding, filling the "VLM online inference" gap in the open-source ecosystem. Nine official demos cover surveillance alerts, live commentary, real-time counting, interactive reading, and other scenarios, proving the "speak-up / stay-silent" logic can really be put to work. OpenMOSS simultaneously releases MOSS-VL-Instruct and MOSS-VL-Base, plus open weights on Hugging Face, which will let live commentary bots, embodied-perception Agents, screen-interpretation tools, and other tracks grow a real "online visual brain".