Argues that using AI agents to automate alignment research could yield confident but misleading safety assessments, because fuzzy, hard-to-supervise tasks resist human oversight, and calls for training agents to handle them.
Formalises and tests wireheading incentives when LLMs' self-grades set their rewards; coupled self-grading inflates grades without accuracy gains, and decoupling mitigates this.