Dimensions overall score 7.0

• The MyEgo dataset is the first egocentric VideoQA dataset designed to evaluate multimodal large language models' ability to understand, remember, and reason about the camera wearer.method 95%
• Top closed-source models (e.g., GPT-5) achieve only about 46% accuracy on the MyEgo dataset, trailing human performance by nearly 40%.result 90%
• Top open-source models (e.g., Qwen3-VL) achieve only about 36% accuracy on the MyEgo dataset, trailing human performance by nearly 50%.result 90%
• Neither explicit reasoning nor model scaling yield consistent improvements in performance on the MyEgo dataset.result 85%
• Models improve when relevant evidence is explicitly provided, but gains drop over time, indicating limitations in tracking and remembering 'me' and 'my past'.limitation 85%
• Competitive multimodal large language models across variants all struggle on the MyEgo dataset.result 80%
• Ego-grounding and long-range memory play crucial roles in enabling personalized question-answering in egocentric videos.technical 80%
• The MyEgo dataset comprises 541 long videos and 5K personalized questions asking about 'my things', 'my activities', and 'my past'.method 95%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card