这篇论文探讨了如何使用多智能体系统进行代码审查，提出了一种新的方法，值得一读。
研究多智能体系统在代码审查中的应用，提出Adversarial Review方法，使用三个智能体进行审查，在LiveCodeBench上优于五智能体基线，在SWE-PRBench上实现最高F1值。
Great paper on multi-agent systems for code review. It's challenging to know how many coding agents...
Great paper on multi-agent systems for code review. It's challenging to know how many coding agents to use to address a problem. The default fix for weak agentic code review is more agents. In turns out that scaling agents to a large number gives diminishing returns on repository-level tasks. This new work tries structured conflict instead. Adversarial Review runs three agents. A main coding agent writes, a reviewer evaluates, and a critic audits the review before any edit are done. On LiveCodeBench it beats a five-agent baseline while using three agents. On SWE-PRBench the naive version exposed a failure mode. The agents converged on agreement without enough evidence behind it. Making disagreement an explicit instruction recovered the highest F1 among tested methods. They also find that cooperative review works when the disagreement is minimal, structured, and grounded in evidence. Paper: arxiv.org/abs/2608.18167 Track more trending AI papers in our academy: academy.dair.ai 💬 2 🔄 0 ❤️ 1 👀 798 📊 2 ⚡