Chinese-Jev: Bringing System One Model to Chinese-Language Tasks

Zexiao Wang; Zihao Zhang; Xudong Wang; Pan Wang; Ziyi Ye; Haoyu Zhao; Zuxuan Wu; Shuicheng Yan

title

authors

Zexiao Wang, Zihao Zhang, Xudong Wang, Pan Wang, Ziyi Ye, Haoyu Zhao, Zuxuan Wu, Shuicheng Yan

date

2026-09-29

venue

arXiv (preprint, not peer reviewed)

arXiv:2609.36965

affiliation

Fudan University

upvotes

16 (Hugging Face Daily Papers, 2026-11-01)

links

abs pdf hf code

abstract

System One models such as Jev offer an efficient alternative to generative language models for tasks that require decisions rather than open-ended responses. However, existing Jev models exhibit limited Chinese-language decision accuracy, restricting their utility in both general and specialized settings. In this paper, we introduce Chinese-Jev, a System One model that addresses this gap through a unified data processing and training pipeline. Our data processing protocol converts heterogeneous Chinese-language annotations into probability targets over candidate options, enabling a shared training formulation across domains and question formats. To enable efficient inference, Chinese-Jev adopts a lightweight encoder-only backbone for text encoding and learns to score candidate answers through decision-oriented training. To address the misalignment between the pre-training distribution and downstream Chinese-language scenarios, we first train the model on a general-purpose corpus of 10 million examples, then fine-tune it separately for the medical, legal, and financial domains. To evaluate decision accuracy and calibration in both general and domain-specific Chinese-language settings, we introduce Chinese-Jev Bench (CJ-Bench). After first-stage pre-training, Chinese-Jev exceeds the accuracy of the closed-source Jev model by 1.24% on general-domain tasks while achieving a 20.3x speedup. Subsequent domain-specific fine-tuning yields a 4.0% accuracy improvement over Jev in medicine and achieves 92% of Jev's average accuracy across specialized domains, with a 17x speedup and an average latency of only 15 ms per example. We further demonstrate on-device deployment of an INT8-quantized model on mobile devices, achieving an inference latency of approximately 1.0 second per decision. The project is available at https://gulucaptain.github.io/Chinese-Jev/.

@misc{wang2026chinese,
  title={Chinese-Jev: Bringing System One Model to Chinese-Language Tasks},
  author={Zexiao Wang and Zihao Zhang and Xudong Wang and Pan Wang and Ziyi Ye and Haoyu Zhao and Zuxuan Wu and Shuicheng Yan},
  year={2026},
  eprint={2609.36965},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2609.36965}
}