New JT-Safe-V2 model enhances AI safety with world-context data

Researchers have introduced JT-Safe-V2, a new foundation model designed to improve the safety and trustworthiness of AI systems. This model integrates general intelligence with safety-by-design principles through enriched data, specialized training procedures, and post-training safety enhancements. Additionally, a framework called Safe-MoMA has been developed to manage multiple models and agents for efficient and traceable inference, reducing costs by over 30% while maintaining performance. The team is releasing the JT-Safe-V2-35B model checkpoint to encourage further research in this area. AI

IMPACT This release offers a new approach to building safer AI models and a framework for more efficient inference, potentially impacting enterprise AI deployments.

RANK_REASON The cluster contains a research paper detailing a new AI model and framework released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• JT-Safe-V2
• Safe-MoMA

• model release
• safety
• paper

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