
During January 2026, Li Jiulong developed the PAIBench-U Perception Score Evaluation feature for video datasets in the EvolvingLMMs-Lab/lmms-eval repository. He engineered an end-to-end workflow in Python, focusing on data processing and metrics aggregation to generate actionable perception scores for video analysis tasks. By implementing robust result processing and integrating YAML-based configuration, Li enabled reproducible quality assurance metrics and streamlined evaluation for video data. His work improved decision-making speed and repository alignment for video dataset QA, laying the foundation for broader adoption and future extensions. The depth of his contribution reflects strong skills in Python, machine learning, and video analysis.

2026-01 Monthly summary: Delivered end-to-end PAIBench-U Perception Score Evaluation for video datasets in lmms-eval, including result processing and metric aggregation; no major bugs reported; overall impact includes improved evaluation capabilities, faster decision-making, and better repository alignment for video data QA. Demonstrated Python data processing, metrics engineering, and Git-based collaboration.
2026-01 Monthly summary: Delivered end-to-end PAIBench-U Perception Score Evaluation for video datasets in lmms-eval, including result processing and metric aggregation; no major bugs reported; overall impact includes improved evaluation capabilities, faster decision-making, and better repository alignment for video data QA. Demonstrated Python data processing, metrics engineering, and Git-based collaboration.
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