Model Releases
Must-read paper from Meta on reliable auto-research agents. If you let coding agents run ML experiments, one silent bug like leaked eval dat…
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 trainin
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: https://academy.dair.ai/papers/rankevolve-a-reliable-multi-agent-auto-research-harness-for-evolving-ranking-mod-2609.39551
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Source: DAIR.AI (X) | 2026-10-03