Worked on integrating the MedXpertQA dataset into the thunlp/SIR-Bench repository, enabling comprehensive benchmarking for medical question answering models. Developed dataset loading and generation configuration using Python and YAML, and extended the evaluation pipeline to support LLM-based judging for the new medical QA corpus. Focused on configuration management and dataset integration, the work included creating standard generation and evaluation configuration files to streamline model assessment. This integration expanded SIR-Bench’s evaluation coverage into the medical NLP domain, allowing for more trusted performance assessments and supporting improvements in medical AI use cases. No bug fixes were recorded during this period.