Xiuyu Li(@sheriyuo):The paper argues that for any ensemble whose final output must be one of the member models' answers, including routing, voting, and MoA, the accuracy is fundamentally capped by the co-failure rate β: acc ≤ 1 − β. It also shows that the commonly reported mean pairwise error correlation (ρ) is insufficient to characterize β, so low correlation alone does not imply large ensemble gains. When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents

Xiuyu Li

@sheriyuo

Researcher @StepFun_ai | Working on long-horizon tasks | Prev @RUC1937 | Opinions are my own

参加 February 2026

1.8K フォロー中 13.4K ファン

Xiuyu Li@sheriyuo

2026.06.27 17:04

The paper argues that for any ensemble whose final output must be one of the member models' answers, including routing, voting, and MoA, the accuracy is fundamentally capped by the co-failure rate β: acc ≤ 1 − β. It also shows that the commonly reported mean pairwise error correlation (ρ) is insufficient to characterize β, so low correlation alone does not imply large ensemble gains. When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents