📹 In an endless video stream, nobody knows which moment will matter later. This model takes that problem head-on. Title: OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction URL: 🧠 Overview A streaming video LLM that turns what it observes into reusable factual memory while deciding when to speak up on its own. At just 4B parameters it takes the top score on all eight streaming video benchmarks tested. 🔥 The problem it solves Keeping past visual tokens around makes them compete with recent observations for limited context, which weakens real-time perception. Keeping only the recent window throws away distant events instead. On top of that, "stay silent" labels dominate the supervision, so learning when to respond gets drowned out. 💡 Methodology The key move is rethinking proactive generation as more than producing user-facing answers: it is also the act of converting evidence into reusable factual records. ・PHCM keeps the visual window short but writes observations out as time-anchored text records, which stay in context long after the source frames are gone ・PSTL keeps supervision at every output anchor while sampling only representative state-change and state-persistence positions 📊 Results ・72.1 on OVOBench and 48.7 on ProactiveVQA, beating the 11B MOSS-VL-Realtime with a 4B model ・On the memory metric ASI it beats full-history retention by 4.0 points while still winning on real-time perception ・PSTL supervises just 27.5% of state tokens yet outperforms dense supervision; random selection at the same 27.5% gains nothing, so it's the choice of positions that matters ・Context length drops 93.1% versus full history, and time to first token falls from 4.560s to 0.124s #VideoUnderstanding# #MultimodalLLM#