Eugene Yonng integrated the MedXpertQA dataset into the thunlp/SIR-Bench repository, enabling comprehensive benchmarking for medical question answering models. He developed dataset loading and generation configuration files in YAML, and extended the evaluation pipeline using Python to support LLM-based judging. This work involved connecting dataset integration with end-to-end evaluation logic, allowing SIR-Bench to assess model performance on medical QA tasks. By focusing on configuration management and medical NLP, Eugene expanded the platform’s evaluation coverage into a new domain. The depth of his contribution lies in enabling trusted, reproducible assessments for medical AI, supporting future improvements in model reliability and accuracy.