# Correlated Errors

In multi-agent coordination, deciding whether a coordinator may act on its inferences by inference accuracy fails in both directions, approving harmful language-model deployments in Hanabi where shared errors compound and rejecting beneficial ones in task assignment where costly errors are rare.

In multi-agent coordination, deciding whether a coordinator may act on its inferences by inference accuracy fails in both directions, approving harmful language-model deployments in Hanabi where shared errors compound and rejecting beneficial ones in task assignment where costly errors are rare.

On long-context tasks, self-consistency, the practice of sampling several answers and aggregating them, no longer improves performance and often lowers it, because positional bias makes the samples repeat the same mistake and the vote turns that mistake into a consensus.

On long-context tasks, self-consistency, the practice of sampling several answers and aggregating them, no longer improves performance and often lowers it, because positional bias makes the samples repeat the same mistake and the vote turns that mistake into a consensus.