DAIR.AI highlights Meta’s RankEvolve, which pairs Claude Code and Codex

DAIR.AI describes RankEvolve, from a Meta paper on reliable auto-research agents, as a system that 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, aimed at silent experiment bugs such as leaked evaluation data or a disconnected gradient.

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, RankEvolve improved NDCG@10 on MovieLens-20M by 4.48% over the published result. The write-up links to the [RankEvolve paper on DAIR.AI Academy](https://academy.dair.ai/papers/rankevolve-a-reliable-multi-agent-auto-research-harness-for-evolving-ranking-mod-2609.39551).