用AI模拟买家在电商页面试错，实验速度从周级缩至小时级。
SimGym: Traffic-Grounded Browser Agents for Offline A/B Testing in E-Commerce
- 用大模型生成带真实购物行为特征的虚拟买家，模拟用户访问。
- 模拟结果与真实用户实验偏差小于5%，验证了高保真度。
- 适合需要快速验证页面改版的电商平台研发团队。
A/B测试仍是评估电商界面改动的金标准，但会分流真实流量、需数周才能得出结论，且可能影响用户体验。我们提出SimGym，一个可扩展的离线A/B测试系统，利用基于大语言模型的合成买家代理，在真实浏览器环境中运行。SimGym从生产交互数据中提取店铺级买家画像与意图，识别出不同行为类型，并在控制组与实验组店铺上模拟加权会话。我们在一家主要电商平台的真实界面改动上，对齐协变量后验证了SimGym的有效性。即使未进行训练后对齐，其代理仍达到当前最优的结果一致性，将实验周期从数周缩短至一小时内，实现无需暴露真实用户即可快速迭代。
原文摘要 · Abstract (English)
A/B testing remains the gold standard for evaluating e-commerce UI changes, yet it diverts traffic, takes weeks to reach significance, and risks harming user experience. We introduce SimGym, a scalable system for rapid offline A/B testing using traffic-grounded synthetic buyers powered by Large Language Model agents operating in a live browser. SimGym extracts per-shop buyer profiles and intents from production interaction data, identifies distinct behavioral archetypes, and simulates cohort-weighted sessions across control and treatment storefronts. We validate SimGym against real human outcomes from real UI changes on a major e-commerce platform under confounder control. Even without alignment post training, SimGym agents achieve state of the art alignment with observed outcome shifts and reduces experiment cycles from weeks to under an hour , enabling rapid experimentation without exposure to real buyers.
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