AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.

翻译：AI编码智能体使实证规范搜索变得快速且廉价，但也增加了隐藏的研究人员自由度。本文基于开源智能体循环架构，将该框架适配至实证经济学工作流，并引入搜索后留存验证环节。在预测组合示例中，多次独立运行的智能体在原始滚动评估中优于标准基准，但并非所有结果都能在搜索后留存验证中保持优势。结合日志记录的搜索过程与留存验证，既可提升自适应规范搜索的透明度，也有助于区分稳健改进与样本特异性发现。