Radhagulhane focused on stabilizing the Mathvision evaluation workflow in the EvolvingLMMs-Lab/lmms-eval repository, addressing reliability issues and enhancing reproducibility for Qwen2.5VL model results. Using Python, they fixed a key evaluation bug, refactored prompt handling to reduce parsing errors, and adjusted evaluation parameters to prevent unintended truncation. Their work in bug fixing and prompt engineering improved the accuracy and consistency of model benchmarking, enabling more reliable comparisons across runs. By streamlining the evaluation process and reducing noise in performance metrics, Radhagulhane supported faster, data-driven decision-making for model tuning, demonstrating depth in model evaluation and workflow robustness.