As our models get more powerful and the associated safety and security risks become bigger, it becomes more and more critical that we can detect potential issues, and quickly stop models in their tracks when needed. To solve this, we built Control Room.

Control Room is a central system to track model workloads across our research infrastructure and stop them quickly when safety or security concerns arise. It connects every research inference workload, including agentic sampling for model training and evaluation, data processing, and more. This works together with our misalignment monitoring systems, and halts model output within 35 seconds of a decision to intervene to stop or pause a job. It has been built to keep working even when other systems fail, and to stop model output if Control Room itself goes down.

Control Room is already deployed across our research infrastructure, where the need is greatest. Our external model-serving stack is already much more centralized by nature, with a well-developed operational capability to quickly take down model deployments if needed.

Control Room works by integrating a few systems: the workload, the inference engine, and the centralized Control Room service.

The workload is the job being run, such as a reinforcement learning experiment, an evaluation, a high-volume sampling or data processing job. We require every research workload that does agentic inference to be associated with an approved safety case.

The inference engine , or just engine , is the model itself. You can think of the engine as the brain of the model, running directly on GPUs.

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