We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehensive safety-by-design paradigm. JT-Safe-V2 emphasizes the joint optimization of general intelligence and safety-by-design through several key innovations: enriching pre-training data with contextual world knowledge, high-certainty pre-training procedures, and safety strengthening post-training mechanisms for enterprise-oriented agentic capabilities. Building on these safety-enhanced foundation models, we propose Safe-MoMA (Safe Mixture of Models and Agents), a framework that enables traceable and efficient inference through the orchestrated deployment of multiple models and agents. Extensive evaluations demonstrate that JT-Safe-V2 achieves state-of-the-art performance across both general intelligence and safety benchmarks. Moreover, Safe-MoMA reduces inference costs by more than 30\% compared to using the largest standalone model baseline while maintaining comparable performance. To facilitate future research on safety-by-design foundation models, we publicly release the post-trained JT-Safe-V2-35B model checkpoint.
@article{arxiv.2605.24414,
title = {JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data},
author = {Junlan Feng and Fanyu Meng and Chong Long and Pengyu Cong and Duqing Wang and Yan Zheng and Yuyao Zhang and Xuanchang Gao and Ye Yuan and Yunfei Ma and Zhijie Ren and Fan Yang and Na Wu and Di Jin and Chao Deng},
journal= {arXiv preprint arXiv:2605.24414},
year = {2026}
}