We describe the Yale-DM-Lab system for the ArchEHR-QA 2026 shared task. The task studies patient-authored questions about hospitalization records and contains four subtasks (ST): clinician-interpreted question reformulation, evidence sentence identification, answer generation, and evidence-answer alignment. ST1 uses a dual-model pipeline with Claude Sonnet 4 and GPT-4o to reformulate patient questions into clinician-interpreted questions. ST2-ST4 rely on Azure-hosted model ensembles (o3, GPT-5.2, GPT-5.1, and DeepSeek-R1) combined with few-shot prompting and voting strategies. Our experiments show three main findings. First, model diversity and ensemble voting consistently improve performance compared to single-model baselines. Second, the full clinician answer paragraph is provided as additional prompt context for evidence alignment. Third, results on the development set show that alignment accuracy is mainly limited by reasoning. The best scores on the development set reach 88.81 micro F1 on ST4, 65.72 macro F1 on ST2, 34.01 on ST3, and 33.05 on ST1.

翻译：本文介绍了耶鲁数据挖掘实验室为ArchEHR-QA 2026共享任务开发的系统。该任务研究患者撰写的住院记录问题，包含四个子任务：临床医生可理解的问题改写、证据句子识别、答案生成以及证据-答案对齐。子任务1采用双模型流水线，结合Claude Sonnet 4与GPT-4o将患者问题改写为临床医生可理解的问题。子任务2-4则基于Azure托管的模型集成（包括o3、GPT-5.2、GPT-5.1和DeepSeek-R1），结合少样本提示与投票策略。实验表明三项主要发现：第一，相比单一模型基线，模型多样性与集成投票能持续提升性能；第二，完整的临床医生答案段落被作为额外提示上下文用于证据对齐；第三，开发集结果表明对齐准确性主要受限于推理能力。在开发集上取得的最佳成绩分别为：子任务4的88.81微平均F1值、子任务2的65.72宏平均F1值、子任务3的34.01分以及子任务1的33.05分。