LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks
- URL: http://arxiv.org/abs/2603.00490v1
- Date: Sat, 28 Feb 2026 06:05:31 GMT
- Title: LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks
- Authors: Hengjian Gao, Kaiwei Zhang, Shibo Wang, Mingjie Chen, Qihang Cao, Xianfeng Wang, Yucheng Zhu, Xiongkuo Min, Wei Sun, Dandan Zhu, Guangtao Zhai,
- Abstract summary: LifeEval is a multimodal benchmark designed to evaluate real-time, task-oriented human-AI collaboration in daily life.<n>LifeEval emphasizes three key aspects: task-oriented holistic evaluation, egocentric real-time perception from continuous first-person streams, and human-assistant collaborative interaction through natural dialogues.
- Score: 71.05217306468857
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid progress of Multimodal Large Language Models (MLLMs) marks a significant step toward artificial general intelligence, offering great potential for augmenting human capabilities. However, their ability to provide effective assistance in dynamic, real-world environments remains largely underexplored. Existing video benchmarks predominantly assess passive understanding through retrospective analysis or isolated perception tasks, failing to capture the interactive and adaptive nature of real-time user assistance. To bridge this gap, we introduce LifeEval, a multimodal benchmark designed to evaluate real-time, task-oriented human-AI collaboration in daily life from an egocentric perspective. LifeEval emphasizes three key aspects: task-oriented holistic evaluation, egocentric real-time perception from continuous first-person streams, and human-assistant collaborative interaction through natural dialogues. Constructed via a rigorous annotation pipeline, the benchmark comprises 4,075 high-quality question-answer pairs across 6 core capability dimensions. Extensive evaluations of 26 state-of-the-art MLLMs on LifeEval reveal substantial challenges in achieving timely, effective and adaptive interaction, highlighting essential directions for advancing human-centered interactive intelligence.
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