JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data
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 infe...
| Authors | Junlan Feng, Fanyu Meng, Chong Long et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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