JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data
The article introduces JT-Safe-V2, a large language model aimed at enhancing the safety and trustworthiness of foundation models. This model builds on its predecessor by incorporating world-context data and innovative safety mechanisms. Additionally, it proposes the Safe-MoMA framework, which improves inference efficiency while maintaining performance standards.
- ▪JT-Safe-V2 is designed to optimize both general intelligence and safety-by-design.
- ▪The model incorporates contextual world knowledge and advanced pre-training procedures.
- ▪Safe-MoMA enables efficient inference through the coordinated use of multiple models and agents.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24414 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24414 (cs) [Submitted on 23 May 2026] Title:JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data Authors:Junlan Feng, Fanyu Meng, Chong Long, Pengyu Cong, Duqing Wang, Yan Zheng, Yuyao Zhang, Xuanchang Gao, Ye Yuan, Yunfei Ma, Zhijie Ren, Fan Yang, Na Wu, Di Jin, Chao Deng View a PDF of the paper titled JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data, by Junlan Feng and 14 other authors View PDF HTML (experimental) Abstract: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.
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