Causal agents get graded on experiments, not answers: ACED-Bench hides the graph and spreads 180 hard tasks across models
ACED-Bench separates natural-language scientific scenarios from hidden probabilistic graphical models so agents must choose observational or interventional experiments within a four-turn budget and commit to causal answers, producing wide spread across models and agent designs on 180 hard tasks while strong agents near ceiling on a 150-question qualification check.