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About this paper
Junlong Tong, Yingqi Fan, Anhao Zhao, Yunpu Ma, Xiaoyu Shen
The StreamingThinker framework developed by researchers at Shanghai Jiao Tong University and Eastern Institute of Technology enables large language models to "think while reading" by concurrently processing input and generating reasoning. This approach reduces token-to-first-token latency by 80% and total response time by over 60% compared to traditional batch thinking, while maintaining comparable reasoning accuracy.
Key insights
- Large Language Models can be effectively adapted to a streaming "think while reading" paradigm, which leverages controllable reasoning depth to progressively improve performance.
- Architectural innovations like Streaming Position Encoding (SPE) and parallel KV caches are crucial for achieving true concurrency between input processing and reasoning generation.
- The framework demonstrates robustness across different input orderings (question-first vs. context-first), adapting its reasoning strategy accordingly.
Summary by alphaXiv.
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