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编码代理使经验性规范搜索变得快速且廉价，但也扩大了隐藏的研究者自由度。本文基于开源代理循环架构，将此框架适配至经验经济学工作流，并增加了搜索后留存样本评估。在预测组合的示例中，多次独立代理运行在原始滚动评估中表现优于标准基准，但并非所有结果在搜索后的留存样本中仍能保持优势。结合日志记录的搜索过程与留存样本评估，既增强了适应性规范搜索的透明度，也有助于区分稳健改进与样本特异性发现。