Robust real-time adversarial monitoring enables inference-time compute paradigms to be rapidly adopted in critical domains like military and healthcare applications.

Inference time failure detection can be reframed as a high-dimensional anomaly detection problem, enabling new ways to place probabilistic bounds on model behavior. Still, traditional anomaly detection is too computationally heavy for inference-time systems, which often generate 100× more data than classic models due to recursive reasoning and large memory use.

However, new inference-time compute paradigms offer higher-level, compressed abstractions, where anomalous outputs can represent semantically meaningful units that are coarser than individual model neurons/parameters. By monitoring these units, we can reduce data dimensionality and build efficient, parallelizable control mechanisms that run during inference. This makes it possible to detect and respond to advanced attacks, such as model layer swapping and prompt injection, in real time.

I’ve been thinking about this paradigm as a central part of AI systems in practice, especially in medicine. Please reach out if you’re interested!