Alignment tests need more compute to expose agent misbehavior
Existing alignment tests may miss harmful agent behavior because eliciting it can require substantial compute. This paper reproduces the behaviors behind the OpenAI-Hugging Face incident with public models, then shows an auditing agent can find similar behavior from high-level descriptions. A simple in-context reinforcement-learning method cuts the compute needed, pointing to automated tests that scale efficiently with available compute.