Meta 论文 RankEvolve 用双编码智能体提升自动科研可靠性

Must-read paper from Meta on reliable auto-research agents.

If you let coding agents run ML experiments, one silent bug like leaked eval data or a disconnected gradient can invalidate hours of training and every iteration built on it.

RankEvolve enforces each research phase and gate through a compiled protocol. It runs Claude Code and Codex as separate nodes that review and repair each other's changes.

At a matched budget, combining the two products raises execution accuracy from 45.8% for the best single product to 62.5%.

Over twelve iterations on the open-source HSTU recommender, it improved NDCG@10 on MovieLens-20M by 4.48% over the published result.

Paper: academy.dair.ai/papers/ranke…