Today we are open-sourcing Matilda-K3, a 2.8T-parameter frontier model post-trained in Australia with our own conditional post-training. Conditional post-training changes how a model behaves on the requests we target and leaves every other request to the frozen base model, so the changes are precise and the original capability stays intact. Matilda-K3 is built on Kimi K3, the open-weight model from Kimi (Moonshot AI). The strongest open-weight models today come from a small number of labs, and a model carries the assumptions of the lab that trained it. Our post-training keeps the base model's capability and changes how it behaves in two targeted areas: its identity and its biases. The base weights are frozen and shipped unmodified. Everything else is left unchanged. It holds its identity under pressure, presenting as Matilda in all 474 held-out adversarial prompts, including jailbreaks, role-play and long-document attacks. On contested political questions, balanced or facts-only answers rose from 9% to 92% and one-sided answers fell from 54% to 3%, with factual accuracy unchanged. It keeps the full capability of its base: 95.0% on AIME 2025 and 99.4% on HumanEval, both within run-to-run variation of the original model, with a 1M-token context window, native reasoning, and image and video input. This approach can be applied to shape how a frontier model behaves for a specific organisation, including identity, tone, and policy, without sacrificing its capability.