# CJ-Bench

The work introduces Chinese-Jev: a unified data pipeline converts heterogeneous Chinese annotations into probability targets over candidate options, a lightweight encoder-only model is first pre-trained on 10 million general Chinese decisions and then fine-tuned separately for medicine, law, and finance, and CJ-Bench is released with 307,900 held-out decisions; after pre-training it reaches 69.20% accuracy on general tasks, a 1.24% relative gain over the closed-source Jev, cuts expected calibration error from 11.45% to 3.78% at 14 ms latency, improves medical accuracy over Jev by 4.0%, reaches 92% of Jev's average accuracy across the three specialized domains at 15 ms average latency, and an INT8-quantized model runs about 1 second per decision in a mobile browser.

The work introduces Chinese-Jev: a unified data pipeline converts heterogeneous Chinese annotations into probability targets over candidate options, a lightweight encoder-only model is first pre-trained on 10 million general Chinese decisions and then fine-tuned separately for medicine, law, and finance, and CJ-Bench is released with 307,900 held-out decisions; after pre-training it reaches 69.20% accuracy on general tasks, a 1.24% relative gain over the closed-source Jev, cuts expected calibration error from 11.45% to 3.78% at 14 ms latency, improves medical accuracy over Jev by 4.0%, reaches 92% of Jev's average accuracy across the three specialized domains at 15 ms average latency, and an INT8-quantized model runs about 1 second per decision in a mobile browser.