The rapid development of GUI foundation models and mobile GUI agents has spurred numerous evaluation benchmarks, yet most rely on simulated environments or open-source applications, leaving real-world closed-source applications largely unevaluated. The core difficulty is that closed-source applications do not expose internal states, making traditional automatic verification inapplicable. To bridge this gap, we introduce AndroidDaily, a large-scale benchmark comprising 350 realistic daily-use tasks across 94 high-frequency Android applications spanning transportation, shopping, local services, entertainment, content creation, social media, and everyday utilities. To enable automatic and verifiable assessment in these opaque environments, we propose Guideline-grounded Reviewer for Automatic Diagnostic Evaluation (GRADE), a process-aware evaluator built on a three-tiered system of observable external guidelines: operational obligations, output quality, and negative constraints. GRADE tracks the agent's visual trajectory against these criteria and produces step-level diagnostic judgments, turning long-horizon, open-ended mobile interactions into verifiable evaluation without relying on hidden internal states. Experiments show that GRADE achieves 87.37\% agreement with human evaluators. The strongest model reaches a 62.0\% success rate on AndroidDaily, highlighting a substantial gap between current reasoning capabilities and practical execution in realistic mobile workflows.

翻译：图形用户界面基础模型与移动GUI智能体的快速发展催生了大量评估基准，然而现有基准大多依赖模拟环境或开源应用，真实世界的闭源应用评估长期处于空白。核心难点在于闭源应用不暴露内部状态，使得传统自动验证方法难以适用。为弥补这一空白，我们提出AndroidDaily——一个覆盖交通、购物、本地服务、娱乐、内容创作、社交媒体及日常工具等94款高频Android应用的350项日常任务的大规模基准测试。针对这些不透明环境中的自动可验证评估，我们提出基于指南的自动诊断评估评审器（GRADE），该过程感知评估器建立在由操作义务、输出质量与负面约束构成的三级可观测外部指南体系之上。GRADE通过追踪智能体视觉轨迹与上述准则的符合程度，生成步骤级诊断判断，在不依赖隐藏内部状态的前提下，将长时域开放型移动交互转化为可验证评估。实验表明，GRADE与人类评估者的判定一致性达87.37%。当前最强模型在AndroidDaily上仅取得62.0%的成功率，揭示了现有推理能力与现实移动工作流执行实践之间的显著差距。