# LLM ensemble

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%.

Emergency departments rarely have time for PTSD screening. PREVIA feeds the transcript of a patient's answer to the routine question "What brought you to the ED?" into a clinically informed ensemble of Llama, Mistral and DeepSeek models arranged in three layers: four clinical dimensions are assessed first, synthesized into a structured summary, then combined with age, sex and trauma type into a 0–100 risk score with a short explanation. Among 145 trauma survivors at two New York City level-1 trauma centers, the ensemble reached AUROC 78.49%±5.75% and sensitivity 95.4%±3.99% for one-month PTSD diagnosis; on 30 randomly selected narratives it scored AUROC 84.39% versus 83.86% for an emergency clinician specialized in PTSD.

Emergency departments rarely have time for PTSD screening. PREVIA feeds the transcript of a patient's answer to the routine question "What brought you to the ED?" into a clinically informed ensemble of Llama, Mistral and DeepSeek models arranged in three layers: four clinical dimensions are assessed first, synthesized into a structured summary, then combined with age, sex and trauma type into a 0–100 risk score with a short explanation. Among 145 trauma survivors at two New York City level-1 trauma centers, the ensemble reached AUROC 78.49%±5.75% and sensitivity 95.4%±3.99% for one-month PTSD diagnosis; on 30 randomly selected narratives it scored AUROC 84.39% versus 83.86% for an emergency clinician specialized in PTSD.