在家庭环境中评估多模态大语言模型的日常复合任务
Evaluating Multimodal Large Language Models with Daily Composite Tasks in Home Environments
AI总结：
本研究在家庭环境中评估了多模态大语言模型在日常复合任务中的表现，发现其在物体理解、空间智能和社会活动领域存在显著差距，为具身MLLMs的发展提供了初步评估框架。
摘要：
人工通用智能（AGI）与传统AI的主要区别在于AGI能够执行需要广泛能力的复合任务。尽管由多模态大语言模型（MLLMs）驱动的具身代理提供了丰富的感知和交互能力，但尚不清楚它们是否能解决复合任务。在本工作中，我们设计了一系列受早期儿童发展过程中常见日常活动启发的复合任务。在动态和模拟的家庭环境中，这些任务涵盖三个核心领域：物体理解、空间智能和社会活动。我们对17种领先的专有和开源MLLMs在这些任务上进行了评估。结果一致显示在所有三个领域表现不佳，表明当前能力与通用智能需求之间存在显著差距。共同，我们的任务提供了一个初步框架，用于评估具身代理的一般能力，标志着具身MLLMs及其实际应用发展的早期但重要的一步。
英文摘要：
A key feature differentiating artificial general intelligence (AGI) from traditional AI is that AGI can perform composite tasks that require a wide range of capabilities. Although embodied agents powered by multimodal large language models (MLLMs) offer rich perceptual and interactive capabilities, it remains largely unexplored whether they can solve composite tasks. In the current work, we designed a set of composite tasks inspired by common daily activities observed in early childhood development. Within a dynamic and simulated home environment, these tasks span three core domains: object understanding, spatial intelligence, and social activity. We evaluated 17 leading proprietary and open-source MLLMs on these tasks. The results consistently showed poor performance across all three domains, indicating a substantial gap between current capabilities and general intelligence requirements. Together, our tasks offer a preliminary framework for evaluating the general capabilities of embodied agents, marking an early but significant step toward the development of embodied MLLMs and their real-world deployment.
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