The model is being used internally to power LLM judges, manage incident response, and screen candidate hypotheses in scientific discovery pipelines. Future updates will include rebasing the tool on other architectures, such as MAI and OpenAI models, to refine latency and quality. This release follows a broader industry trend of deploying specialized decision-making architectures to outperform general-purpose large language models.
