Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously

Online Video Large Language Models (VideoLLMs) playa critical role in supporting responsive, real-time interaction. Existingmethods focus on streaming perception, lacking a synchronized logicalreasoning stream. However, directly applying test-time scaling methodsincurs unacceptable response latency. To address this trade-off, we pro-pose Video Streaming Thinking (VST), a novel paradigm for streamingvideo understanding. It supports a thinking-while-watching mechanism,which activates reasoning over incoming video clips during streaming.This design improves timely comprehension and coherent cognition whilepreserving real-time responsiveness by amortizing LLM reasoning latencyover video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts theoffline VideoLLM to causal streaming reasoning, and VST-RL, whichprovides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an auto-mated training-data synthesis pipeline that uses video knowledge graphsto generate high-quality streaming QA pairs, with an entity–relationgrounded streaming Chain-of-Thought to enforce multi-evidence reason-ing and sustained attention to the video stream. Extensive evaluationsshow that VST-7B performs strongly on online benchmarks, e.g. 79.5%on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remainscompetitive on offline long-form or reasoning benchmarks. Comparedwith Video-R1, VST responds 15.7× faster and achieves +5.4% improve-ment on VideoHolmes, demonstrating higher efficiency and strong gen-eralization across diverse video understanding tasks. Code, data, andmodels have been released at https://github.com/1ranGuan/VST.