让AI自己发明检索词，搜索准确率暴涨36%
现在的搜索系统，AI只负责把问题变长变短，真正找东西的还是老一套关键词匹配。这篇反着来：让AI直接生成查询和商品两边的关键词，两边用强化学习互相较劲、共同进化——查询侧生成的关键词好不好，直接看能不能搜到目标；商品侧生成的关键词好不好，看它有没有让查询侧搜得更准。两边都在优化同一个目标，结果在公开数据集上比最强基线高了36%。它不是你明天能用上的，但指向一个方向：AI不再给旧系统打下手，而是自己发明一套检索语言。
📄 原文摘要(英文)
Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large item universe for downstream ranking and auction. Recent work increasingly leverages LLMs to improve retrieval through query expansion, data synthesis, and retrieval-feedback training. However, the generative component is typically used for query-side augmentation, while final matching is still delegated to a downstream retriever. We introduce CoGR, a retrieval framework that instead trains LLMs to directly construct retrieval representations on both query and item sides. Each generator produces a compact set of keywords, which are matched directly through an inverted index, preserving compatibility with existing keyword-based retrieval infrastructure. CoGR uses a two-stage training pipeline. Supervised fine-tuning first establishes an aligned keyword space, after which co-evolving reinforcement learning alternately optimizes the query- and item-side generators with GRPO against the opposite side's frozen index. Both sides optimize the same query-to-item retrieval F_1 objective: the query side receives retrieval F_1 directly, while the item side receives a counterfactual marginal reward measuring the change in query-side F_1 caused by its generated keywords. Across 10 representative sparse, dense, and generative baselines, CoGR achieves the best performance on both an internal APP Marketplace dataset and the public WANDS benchmark, improving F_1 over the strongest baseline by 10.9% and 36.1%, respectively. Further analysis shows stable co-evolution and increasingly aligned query--item keyword spaces over training.