LifeEval: A Multimodal Benchmark for Assistive AI in Egocentric Daily Life Tasks
arXiv:2603.00490v1 Announce Type: new 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 a
arXiv:2603.00490v1 Announce Type: new 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.
Executive Summary
This article introduces LifeEval, a novel multimodal benchmark designed to evaluate real-time, task-oriented human-AI collaboration in daily life from an egocentric perspective. LifeEval fills a significant gap in existing video benchmarks by emphasizing task-oriented holistic evaluation, egocentric real-time perception, and human-assistant collaborative interaction through natural dialogues. The benchmark comprises 4,075 high-quality question-answer pairs across 6 core capability dimensions. Extensive evaluations of 26 state-of-the-art Multimodal Large Language Models (MLLMs) on LifeEval reveal substantial challenges in achieving timely, effective, and adaptive interaction, highlighting essential directions for advancing human-centered interactive intelligence. The research has significant implications for the development of assistive AI systems that can effectively augment human capabilities in dynamic, real-world environments.
Key Points
- ▸ LifeEval is a multimodal benchmark designed to evaluate real-time, task-oriented human-AI collaboration in daily life.
- ▸ The benchmark emphasizes task-oriented holistic evaluation, egocentric real-time perception, and human-assistant collaborative interaction.
- ▸ LifeEval comprises 4,075 high-quality question-answer pairs across 6 core capability dimensions.
Merits
Comprehensive Evaluation Framework
LifeEval provides a rigorous and comprehensive evaluation framework for assessing the capabilities of Multimodal Large Language Models (MLLMs) in real-world environments.
State-of-the-Art MLLMs Evaluations
The article presents extensive evaluations of 26 state-of-the-art MLLMs on LifeEval, providing valuable insights into the challenges and limitations of current AI systems.
Demerits
Limited Scope
The article focuses primarily on Multimodal Large Language Models (MLLMs) and may not generalize to other types of AI systems or real-world environments.
Annotational Pipeline
The article relies on a rigorous annotational pipeline to construct the benchmark, which may introduce biases or limitations in the evaluation framework.
Expert Commentary
The introduction of LifeEval marks a significant step forward in the evaluation of Multimodal Large Language Models (MLLMs) and the development of human-centered interactive intelligence. While the article presents a comprehensive evaluation framework and extensive evaluations of state-of-the-art MLLMs, it also highlights the need for further research in this area. The development of assistive AI systems that can effectively augment human capabilities in dynamic, real-world environments requires a more nuanced understanding of the challenges and limitations of current AI systems. The article's findings and the creation of LifeEval provide a valuable foundation for this research agenda.
Recommendations
- ✓ Future research should focus on developing more nuanced and comprehensive evaluation frameworks for assessing the capabilities of MLLMs in real-world environments.
- ✓ The development of more advanced AI systems that can adapt to changing environments and user needs is critical for the creation of effective assistive AI systems.