During July 2025, Guang Yang contributed to the liguodongiot/transformers repository by developing an Enhanced Beam Search feature with Per-Instance Early Stopping. This work focused on improving the reliability and efficiency of text generation by marking finished instances within a batch, preventing unnecessary computation and ensuring more consistent outputs. Yang implemented the solution in Python, leveraging expertise in machine learning and natural language processing to align the generation process with business needs for scalability and predictability. The approach demonstrated thoughtful engineering depth, addressing both computational efficiency and output stability in batch generation scenarios without introducing unnecessary complexity.