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. We ran a controlled experiment: 30 artifacts (code, technical documents, presentation scripts) with 150 injected errors, tested under four review conditions -- same-session Self-Review (SR), repeated Self-Review (SR2), context-aware Subagent Review (SA), and Cross-Context Review (CCR). The central result is that a second review helps only when it happens in a fresh session: CCR (F1 2...
| Authors | Tae-Eun Song |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 1 October 2026 |
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