Distilling Long-Video Perception into Sub-2B Vision-Language Models

A 2B parameter vision-language model achieved 0.8279 on the EgoLongQA test set by distilling teacher traces from a larger agentic pipeline. The student model reaches 89% of the teacher's accuracy using only 1.1% of its parameters, enabling long-video understanding in highly constrained edge environments.