Thinking in Streaming Video

Real-time understanding of continuous video streams is es-sential for interactive assistants and multimodal agents operating in dy-namic environments. However, most existing video reasoning approachesfollow a batch paradigm that defers reasoning until the full video con-text is observed, resulting in high latency and growing computationalcost that are incompatible with streaming scenarios. In this paper, weintroduce ThinkStream, a framework for streaming video reasoningbased on a Watch–Think–Speak paradigm that enables models toincrementally update their understanding as new video observations ar-rive. At each step, the model performs a short reasoning update and de-cides whether sufficient evidence has accumulated to produce a response.To support long-horizon streaming, we propose Reasoning-CompressedStreaming Memory (RCSM), which treats intermediate reasoning tracesas compact semantic memory that replaces outdated visual tokens whilepreserving essential context. We further train the model using a Stream-ing Reinforcement Learning with Verifiable Rewards scheme that alignsincremental reasoning and response timing with the requirements ofstreaming interaction. Experiments on multiple streaming video bench-marks show that ThinkStream significantly outperforms existing onlinevideo models while maintaining low latency and memory usage. Code,models and data are available at https://github.com/CASIA- IVA-Lab/ThinkStream.