ElderBench：面向老年人的自主移动智能体基准测试

中文摘要 AI 辅助

自主移动智能体在协助老年人使用智能手机方面具有巨大潜力，但现有的图形用户界面（GUI）基准主要依赖明确的目标导向指令，很少捕捉老年用户自然出现的语言模式，如间接言语、指代歧义以及规格不明确的请求。基准指令与现实世界中老年人交互之间的这种不匹配可能会阻碍智能体的可靠部署。为解决这一差距，我们推出了ElderBench，这是首个针对面向老年人的真实场景评估移动GUI智能体的基准测试。ElderBench由从20个应用程序的老年人处收集的249项自然产生的智能手机任务构建而成。我们首先从句法、语义和语用角度描述了老年指令与现有GUI基准指令之间的语言差异。随后，我们在在线和离线设置下评估了主流GUI智能体和视觉语言模型，发现它们在处理面向老年人的指令时会出现显著的性能下降。通过受控指令归一化、失败分析和细粒度语言特征分析，我们进一步确定了老年特定语言模式如何导致智能体失败。我们的研究结果为开发更具适应性、可解释性和年龄包容性的面向老年人的GUI智能体提供了可行的设计见解。

英文摘要

While autonomous mobile agents hold great potential for assisting older adults with smartphone usage, existing GUI benchmarks mainly rely on explicit, goal-oriented instructions and rarely capture the naturally occurring language patterns of older users, such as indirect speech, referential ambiguity, and under-specified requests. This mismatch between benchmark instructions and real-world elderly interactions may hinder reliable agent deployment. To address this gap, we present ElderBench, the first benchmark for evaluating mobile GUI agents in authentic elderly-oriented scenarios. ElderBench is constructed from 249 naturally elicited smartphone tasks collected from older adults across 20 applications. We first characterize the linguistic divergence between elderly instructions and existing GUI benchmark instructions from syntactic, semantic, and pragmatic perspectives. We then evaluate mainstream GUI agents and Vision-Language Models under both online and offline settings, revealing substantial performance degradation when handling elderly-oriented instructions. Through controlled instruction normalization, failure analysis, and fine-grained linguistic feature analysis, we further identify how elderly-specific language patterns contribute to agent failures. Our findings provide actionable design insights toward more adaptive, interpretable, and age-inclusive GUI agents for older adults.

发表机构

• Fudan University（复旦大学）

机构由 AI 辅助整理，请以论文原文为准。

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19 pages, 5 figures