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Sub-2B Model Wins EgoLongQA Challenge with 89% of Larger Pipeline's Accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a sub-2 billion parameter model that achieved first place in the <=2B parameter division of the EgoLongQA track at the Wearable-AI Challenge, held as part of ECCV 2026. This compact model, derived from distilling a larger tool-using agentic pipeline, can process ten-minute egocentric videos and answer multiple-choice questions in a single forward pass. Despite its significantly reduced size, it reached 89% of the accuracy of the larger pipeline, utilizing only 1.1% of its parameters. AI

IMPACT Demonstrates effective distillation techniques for creating smaller, efficient models capable of complex video understanding tasks.

RANK_REASON Research paper detailing a model's performance in a specific challenge track. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Sub-2B Model Wins EgoLongQA Challenge with 89% of Larger Pipeline's Accuracy

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Research paper detailing a model's performance in a specific challenge track. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Logesh Kumar Umapathi ·

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

    arXiv:2609.07154v2 Announce Type: replace Abstract: 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…

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