vladimirekhin-sketch/video-llm-evaluation-harness

The project is currently in its infancy with 0 stars and 0 forks, having been created only 10 days ago. It addresses a highly relevant but crowded niche: Video-LLM evaluation. While the need for standardized video evaluation is high, this project lacks any unique technical moat or data gravity. It competes directly with established benchmarking suites like Video-MME, OpenCompass, and the EleutherAI LM-Evaluation-Harness (which is expanding into multimodal). Frontier labs (OpenAI, Google) maintain internal, highly optimized evaluation pipelines and are moving toward 'LLM-as-a-judge' patterns for video, which makes standalone metric scripts less defensible. Without significant community adoption or the inclusion of proprietary, hard-to-access datasets, this repo remains a personal tool or a reference implementation rather than a defensible platform. Platform domination risk is high because major cloud providers (Vertex AI, AWS Bedrock) are increasingly integrating evaluation suites directly into their model-garden offerings.