We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (MLLMs). EgoBlind comprises 1,210 videos that record the daily lives of real blind users from a first-person perspective. It also features 4,927 questions directly posed or generated and verified by blind individuals to reflect their needs for visual assistance under various scenarios. We provide each question with an average of 3 reference answers to alleviate subjective evaluation. Using EgoBlind, we comprehensively evaluate 15 leading MLLMs and find that all models struggle, with the best performers achieving accuracy around 56\%, far behind human performance of 87.4\%. To guide future advancements, we identify and summarize major limitations of existing MLLMs in egocentric visual assistance for the blind and provide heuristic suggestions for improvement. With these efforts, we hope EgoBlind can serve as a valuable foundation for developing more effective AI assistants to enhance the independence of the blind individuals' lives.

本研究提出了EgoBlind，这是首个针对盲人的自我中心视频问答数据集，以评估当代多模态大语言模型的辅助能力。研究发现，现有模型在视觉辅助方面存在显著局限性，准确率仅为56%，远低于人类的87.4%。此工作为未来开发更有效的AI助手提供了重要的基础和改进建议。

EgoBlind：为盲人提供自我中心视觉辅助的研究