SOURCE-LINKED INTELLIGENCE

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

We describe our entry to the EgoLongQA track of the Wearable-AI Challenge in ECCV 2026, which placed first in the <=2B parameter division with 0.8279 on the held-out test set. Our system is a single 2B vision-language model that answers multiple-choice questions about ten-minute egocentric videos in one greedy forward pass; It is obtained by distilling the junior perception module of a tool-using agentic pipeline, not the agent itself into a small student, using teacher traces filtered to those that answered correctly. it reaches 89% of the accuracy of the large agentic pipeline using 1.1% of

Read original source ↗ Open in workspace

recordType

paper

region

Global