Cross-Context Review: Improving LLM Output Quality by Separating Production and Review Sessions

Large language models struggle to catch errors in their own outputs when the review happens in the same session that produced them. This paper introduces Cross-Context Review (CCR), a straightforward method where the review is conducted in a fresh session with no access to the production conversation history. Over 360 reviews across four conditions, CCR achieved an F1 of 28.6%, significantly outperforming same-session Self-Review (24.6%, p=0.008), repeated Self-Review (21.7%, p<0.001), and context-aware Subagent Review (23.8%, p=0.004). The SR2 result rules out repetition as an explanation — the benefit comes from context separation itself.