Adversarial Review：结构化冲突提升多智能体代码审查效率
Great paper on multi-agent systems for code review.
揭示了多智能体协作中的关键失效模式（虚假共识），并给出了通过结构化冲突提升性能的具体方法，对构建复杂 Agent 系统有直接参考价值。
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: https://arxiv.org/abs/2608.18167
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