xjtupanda/Tango

Tango is a research-oriented repository associated with a paper from Xi'an Jiaotong University. While the goal—making Video LLMs (VLLMs) more efficient—is a critical bottleneck in the field, the project currently lacks any significant defensive moat. With only 2 stars and 4 days of age, it is in the very early stages of dissemination. The VLLM efficiency space is extremely crowded with competing approaches like Video-LLaVA, LLaVA-NeXT, and various token-pruning methods (e.g., Token Merging). Frontier labs like Google (Gemini 1.5 Pro) and OpenAI are aggressively optimizing their long-context video windows, making this type of architectural optimization a high-risk area for obsolescence. A technical competitor could implement the core 'taming' logic within weeks of reading the paper. The lack of a packaging strategy (no pip install) or developer-friendly API further limits its utility beyond being a reference for other researchers. Its primary value is academic, and its 'displacement horizon' is very short as new state-of-the-art efficiency techniques for video emerge monthly.