Curved Inference II: Sleeper Agent Geometry - Extending Interpretability Beyond Probes

This paper extends Anthropic's Sleeper Agents research, introducing a naturalistic methodology for detecting deceptive alignment in models. It uses multi-turn context windows and a new metric called semantic surface area to analyze geometric patterns in model outputs, suggesting a scalable, unsupervised path for detection beyond linear methods.