Cross-model adversarial checking outperforms single-model self-verification for LLM reliability

Short summary

The author argues that self-checking by a single AI model is unreliable because repeated runs share the same blind spots. The solution is to use two different AI models where one is asked to refute the other's output, not review it. The author also describes a frustration-triggered automation that invokes cross-model checks when the user notices repeated wrong answers, and warns that model-generated summaries can launder errors before the second model sees them.

• •Single-model self-checking fails because repeated runs share the same blind spots
• •Cross-model adversarial checking (ask to refute, not review) catches errors self-checking misses
• •Frustration-triggered automation can invoke cross-model checks, but only after the first wrong answer lands

Generated with AI, which can make mistakes.