Month 2026-04: Delivered a new video-based MME-V2 evaluation framework for multimodal LLMs in huggingface.js, expanding benchmarking coverage without altering runtime data paths. Implemented by extending the EVALUATION_FRAMEWORKS constant in packages/tasks/src/eval.ts with the video-mme-v2 entry, including its name, description, and GitHub URL. The change is tracked in PR #2097 and committed as dc1c13ab9ee0ba07532ea661680b438c23ffebd8, demonstrating a low-risk, backward-compatible extension. Major bugs fixed: none reported this month. Impact: enables broader evaluation of video-enabled models, improving model selection, benchmarking, and customer value. Technologies/skills demonstrated: TypeScript, codebase extensibility, careful config augmentation, and collaboration with automated review processes (Cursor Bugbot).

July 2025 — EvolvingLMMs-Lab/lmms-eval: Delivered a new Video-TT Benchmark for video-text understanding with GPT-based evaluation to standardize business-relevant multi-modal model assessment. Implemented configuration files, data processing utilities, and GPT-based evaluation models to enable scalable, reproducible benchmarking across audio, text, and video modalities. Focused on maintainability and traceability to support future improvements and partner integrations; no major bugs reported this month.