Who Is Worth Waiting For? Decision Closure in Heterogeneous Language Model Ensembles

A heterogeneous language model ensemble can reach a fixed decision before all its models finish: individual outputs may remain uncertain even when every compatible combination gives the same answer. We use this condition to stop unfinished calls while preserving the complete-panel decision. Split conformal prediction converts native token prefixes into nested sets of possible terminal outputs. The controller stops when these sets imply a unique decision and, before then, cancels individual calls that can no longer change it. We prove reference-decision fidelity on the joint coverage event for any fixed deterministic rule over finite outputs. Experiments with ten open models on six public benchmarks show wall-time reductions of 34.9% to 53.6% in paired low-load trials. On two complete science-question test splits totaling 1,672 questions, both panels save 49.0% to 71.0% of generated tokens and retain both reference decisions on 99.8% of questions. Weighted voting and strict majority with deferral also benefit from the same stopping condition.