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About this paper
Zikang Liu, Longteng Guo, Handong Li, Ru Zhen, Xingjian He, Ruyi Ji, Xiaoming Ren, Yanhao Zhang and 2 more
ThinkStream, a unified framework for real-time video understanding, introduces an incremental Watch–Think–Speak paradigm to process continuous streams efficiently. The framework achieved an average score of 59.66 on OVO-Bench with its 3B model, outperforming existing online models, and successfully reduced end-to-end inference latency below 0.5s for 2 FPS inputs, enabling practical real-time operation.
Key insights
- Incremental reasoning, where models generate short, localized thoughts, effectively enables real-time understanding and decision-making in dynamic video streams.
- Intermediate reasoning traces can serve as a highly compressed and semantically rich long-term memory, significantly mitigating the unbounded growth of visual tokens in long-horizon video processing.
- Training with verifiable, rule-based rewards in a streaming reinforcement learning setup successfully guides models to produce accurate, structurally compliant, and temporally precise responses for real-time interaction.
Summary by alphaXiv.
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