让AI搜索动态适应商品库存变化，无需训练即可提升点击率。
Inventory-Grounded Policy-Level Optimization for Training-Free AI Search
- 基于实时库存生成策略指南，动态调整搜索行为
- 线上测试点击率提升3.17%，错误案例减少38.9%
- 适合高频率更新商品库的智能搜索系统
AI搜索系统在部署初期通常面临频繁更新的商品目录，可用商品及其属性难以作为稳定知识固化在固定提示或策略中。微调、强化学习和静态提示修补均不适用：标签稀缺、奖励随库存漂移、模型发布成本高，提示修复迅速过时。本文提出无需训练的库存引导策略优化（IGPO），将策略与环境事实分离：学习基于运行时库存证据的策略指南，而非记忆具体商品。在线阶段，通过探测库存构建库存画像，并将相关策略指南注入检索与选择提示；离线阶段，按查询分组随机回溯，混合结果直接提供对比信号，库存引导探索循环可区分未覆盖检索路径与无匹配项的情况。自2026年5月起，IGPO已部署于商业智能助手搜索系统。14天线上A/B测试显示，完整方案相对点击率提升3.17%，审计错误案例减少38.9%。
原文摘要 · Abstract (English)
Early in deployment, an AI search system typically operates over a frequently updated product catalog, so the available items and their properties cannot be treated as stable knowledge that can be encoded in fixed prompts or strategies. Fine-tuning, reinforcement learning, and static prompt patches fit poorly: labels are scarce, rewards drift with inventory, model releases are costly, and prompt fixes quickly stale. We present Inventory-Grounded Policy-Level Optimization (IGPO), a training-free approach for fixed AI search pipelines. IGPO separates policy from environment facts: it learns Policy Guidelines for acting on runtime inventory evidence rather than memorizing available items. Online, IGPO grounds each query by probing the inventory and constructing an inventory portrait, then injects relevant Policy Guidelines into the retrieval and selection prompts. Offline, stochastic rollouts are grouped by query -- mixed outcome groups directly yield contrastive signal, and an inventory-guided exploration loop distinguishes missed retrieval routes from cases where no matching support is found under the observed inventory evidence. Since May 2026, IGPO has been deployed in a commercial smart-assistant AI search system. A 14-day online A/B test of the complete IGPO treatment shows a 3.17% relative CTR lift and a 38.9% reduction in audited bad cases.
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