Today we release mammoth-1 and elephant-1! We post-trained mammoth-1, the #1 ranked best decision model in the world, and elephant-1, which is the best model under 15B (and beats Jev). We're open-sourcing all model weights and provide a technical report detailing all our training and data methods.

We've collaborated with @aiunderwriting on integrating new controls for open-weight models into the standard for agent security, safety and reliability. Our focus at Base Labs is to build a frontier safety stack for open-source models. To deploy these models with confidence, organizations need safety controls at three layers: the organizational, application and model. The new AIUC-1 model-layer controls cover the safe use and deployment of open-weight models, including: - Pre-deployment testing of model changes - Verification of model source, integrity and license - Cyber evaluations on modified open-weight models We believe openness is an advantage for safety: open weights can easily be tested, monitored and verified. Standards are how that becomes the norm, and they're another piece of the stack we're excited to work toward at Base Labs. Thank you to @RuneKvist , @RajivDattani and the AIUC-1 team – we look forward to expanding our technical collaboration.

New weekly Rollout from the Base Labs team. We ask: why do models forget facts that they iteratively learn? This is an extension of our NeurIPS workshop submission and uses talkie, a 13B model trained only on pre-1931 text (Levine et al., 2026).