Current embodied VLM evaluation relies on static, expert-defined, manually annotated benchmarks that exhibit severe redundancy and coverage imbalance. This labor intensive paradigm drains computational and annotation resources, inflates costs, and distorts model rankings, ultimately stifling iterative development. To address this, we propose Agentic Automatic Evaluation (A2Eval), the first agentic framework that automates benchmark curation and evaluation through two collaborative agents. The Data Agent autonomously induces capability dimensions and assembles a balanced, compact evaluation suite, while the Eval Agent synthesizes and validates executable evaluation pipelines, enabling fully autonomous, high-fidelity assessment. Evaluated across 10 benchmarks and 13 models, A2Eval compresses evaluation suites by 85%, reduces overall computational costs by 77%, and delivers a 4.6x speedup while preserving evaluation quality. Crucially, A2Eval corrects systematic ranking biases, improves human alignment to Spearman's rho=0.85, and maintains high ranking fidelity (Kendall's tau=0.81), establishing a new standard for high-fidelity, low-cost embodied assessment. Our code and data will be public soon.

翻译：当前具身视觉语言模型评估依赖于静态、专家定义且人工标注的基准测试集，这些数据集存在严重的冗余性和覆盖不均衡问题。这种劳动密集型范式消耗了大量计算与标注资源，推高了成本，扭曲了模型排名，最终阻碍了迭代开发进程。为解决这一问题，我们提出了智能体化自动评估框架A2Eval，这是首个通过两个协同智能体实现基准测试自动构建与评估的智能体化框架。数据智能体自主归纳能力维度并构建平衡紧凑的评估套件，而评估智能体则综合验证可执行的评估流程，实现完全自主的高保真度评估。在10个基准测试集和13个模型上的实验表明，A2Eval将评估套件压缩了85%，整体计算成本降低77%，在保持评估质量的同时实现了4.6倍的加速。关键的是，A2Eval修正了系统性排名偏差，将人类对齐度提升至斯皮尔曼相关系数ρ=0.85，并保持高排名保真度（肯德尔τ系数=0.81），为高保真、低成本的具身评估确立了新标准。我们的代码与数据即将公开。