Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods often couple semantic expansion with retrieval decisions in short-horizon inference loops, leading to high inference cost and limited suitability for time-sensitive news retrieval. We propose DynaTree, a two-stage framework for efficient and adaptive news retrieval. In the offline stage, DynaTree uses coordinated agents to construct a reusable retrieval tree that materializes the semantic space of a query topic. In the online stage, DynaTree performs lightweight daily subtree selection over a time-localized evaluation proxy, without further agentic reasoning, tree modification, or retraining. Experiments on a multi-day Syft news benchmark and multiple BEIR datasets show that DynaTree achieves strong recall and ranking performance, consistently outperforming standard RAG and prior agentic baselines. We further deploy DynaTree in the Syft production system and evaluate it through online A/B testing from Jan. 28 to Feb. 6, 2026. The dynamically adapted variant improves survival rate from 0.32-0.53 to 0.59-0.73 over a fixed offline-selected subtree and outperforms existing production recallers on every evaluation day. These results show that persistent, structure-aware semantic expansion can translate offline agentic reasoning into practical improvements in coverage, freshness, and relevance for real-world news retrieval.

翻译：智能检索增强生成通过整合规划、工具使用和迭代推理来改进检索，但现有智能RAG方法常将语义扩展与检索决策耦合在短时域推理循环中，导致推理成本高且不适合时效性新闻检索。我们提出DynaTree——一种高效自适应新闻检索的两阶段框架。离线阶段，DynaTree利用协同智能体构建可复用的检索树，具象化查询主题的语义空间；在线阶段，DynaTree通过时间局部化评估代理进行轻量级的每日子树选择，无需额外智能体推理、树结构修改或重新训练。在多日Syft新闻基准和多个BEIR数据集上的实验表明，DynaTree实现了强大的召回率和排序性能，持续优于标准RAG和先前智能基线。我们进一步将DynaTree部署至Syft生产系统，并通过2026年1月28日至2月6日的在线A/B测试进行评估。动态自适应变体将生存率从0.32-0.53提升至0.59-0.73（对比固定离线选择子树），并在每个评估日超越现有生产召回器。这些结果表明，持久化的结构感知语义扩展可将离线智能体推理转化为覆盖度、新鲜度和相关性方面的实际改进，适用于真实新闻检索场景。