Confucius4-R2T2: Stable-Prefix ASR Model for Real-Time Transcription

Researchers have unveiled Confucius4-R2T2, a stable-prefix streaming automatic speech recognition (ASR) model designed for real-time transcription. Published on November 10, 2026, on arXiv and highlighted on HuggingFace Daily Papers, the model introduces a significant advancement in streaming ASR by ensuring that once text is emitted, it remains immutable, all while maintaining high accuracy and low latency. Confucius4-R2T2, short for Real Real-Time Transcription, adapts the Qwen3-ASR-1.7B model through post-training on 1,500 hours of Chinese and English audio. The training process employs the Longest Stable Prefix (LSP) learning method, a novel concept introduced by the researchers. LSP ensures that the model's output is monotonic, consistent with full-audio transcription, and maximally stable. This approach allows the model to convert from a full-context ASR to a stable-prefix streaming ASR without architectural modifications.

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