This report synthesises findings from 1 peer-reviewed paper addressing the following research question: What is the efficiency-accuracy trade-off when deploying Deepseek R1 and Claude in secure code review pipelines, measured by inference latency and vulnerability detection F1-scores on the Big-Vul. Large language models (LLMs) have demonstrated strong capability for code understanding and vulnerability detection. However, most existing approaches rely on static prompting and treat the model as a passive predictor, limiting adaptability under uncertainty, particularly in. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the efficiency-accuracy trade-off when deploying Deepseek R1 and Claude in secure code review pipelines, measured by inference latency and vulnerability detection F1-scores on the Big-Vul dataset? Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.

Machine-generated literature synthesis. Content is derived from peer-reviewed papers; see individual sources for authoritative data. Automated review score: 9.2/10. Published by Assignee Research (https://assignee.net).