Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models

Multimodal large language models (MLLMs) have demon-strated strong performance in offline video understanding tasks, but mostremain constrained to offline inference or exhibit weak online reasoningability, rendering online multi-turn interaction over continuously arriv-ing video streams challenging. Existing streaming approaches adopt aninterleaved perception-generation paradigm, which precludes concurrentperception and generation and induces early memory decay with grow-ing streams, degrading long-range dependency modeling. We proposeThink While Watching, a memory-anchored streaming video reason-ing framework that maintains continuous segment-level memory duringmulti-turn interaction. We construct a three-stage, multi-round, chain-of-thought (CoT) dataset with a stage-matched training strategy while en-forcing strict causality in streaming reasoning via a segment-level stream-ing causal mask and streaming positional encoding. At inference, we de-sign an efficient pipeline that overlaps watching and thinking and adap-tively selects the optimal attention backend. We evaluate our methodunder single-round and multi-round streaming input protocols. Basedon Qwen3-VL, we improve single-round accuracy by 2.6% on Streaming-Bench and 3.79% on OVO-Bench. In the multi-round protocol, we main-tain performance while reducing output tokens by 56%. Code is avail-able at: https://github.com/wanglu-cs/ThinkWhileWatching.