During July 2025, Qingsheng Zeng focused on enhancing the reliability of model evaluation in the EvolvingLMMs-Lab/lmms-eval repository. He addressed a bug in the ScienceQA post-processing evaluation logic by implementing a case-insensitive exact-match comparison and supporting predictions that begin with a letter followed by a period. Using Python and leveraging skills in bug fixing and natural language processing, he improved the accuracy of predicted versus target answer comparisons. This work reduced false mismatches and edge-case failures, resulting in more trustworthy QA metrics for benchmarking. The depth of the fix supports faster, data-driven model refinement and evaluation stability.