Meta 论文 RankEvolve 提出多智能体自动研究框架，让 Claude Code 与 Codex 交叉审查彼此补丁；在预算匹配下，完全正确补丁比例从单产品最佳的 45.8% 提升到 62.5%。论文指出，异构智能体的错误更不相关，而同产品智能体容易以相同方式失败，因此交叉审查更能发现训练代码中的静默缺陷。论文地址为 arxiv.org/abs/2609.39551。
New Meta paper finds that having 2 different coding agents review each other's patches catches far more silent bugs than giving 1 agent a bigger budget.
Mixing Claude Code and Codex on the same task raised fully correct patches from 45.8% to 62.5% at matched spend.
Agents editing real training code can leak test data, break a gradient, or miswire a flag.
The code still runs, so you burn GPU hours and get numbers that look valid.
The reason is uncorrelated mistakes: agents from the same product fail the same way, so there is little for review to catch. The effect repeated on an unrelated training codebase.
– arxiv. org/abs/2609.39551
Title: "RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models"
来源：Rohan Paul · x.com