Saul Urso

PISA-E decides both which large language models to combine and how many to invoke for each prompt at inference time, reusing PISA's per-model capability estimates to predict ensemble success without training separate estimators for model combinations. Across four routing benchmarks it reaches macro-average task performance 2.09 percentage points above the strongest ensemble competitor in-distribution and 2.87 points out-of-distribution when quality is prioritized, and at selected lower-cost operating points it also cuts average inference cost by roughly 36% and 25%.

PISA-E decides both which large language models to combine and how many to invoke for each prompt at inference time, reusing PISA's per-model capability estimates to predict ensemble success without training separate estimators for model combinations. Across four routing benchmarks it reaches macro-average task performance 2.09 percentage points above the strongest ensemble competitor in-distribution and 2.87 points out-of-distribution when quality is prioritized, and at selected lower-cost operating points it also cuts average inference cost by roughly 36% and 25%.