她们在这里，有一个属于自己的柜子，一个固定的位置，一个每周见几次面的教练。教练记得她们的孩子多大了，记得她们什么时候来例假会腰疼，记得她们不喜欢哪个动作。
\u5e73\u53f0\u5217\u8868 | \u793e\u4ea4\u5a92\u4f53 | \u5e73\u53f0\u4ecb\u7ecd | \u8054\u7cfb\u6211\u4eec | \u5e7f\u544a\u4fe1\u606f | RSS | \u8fd0\u8425\u65b9\u9488 | \u62db\u8058\u4fe1\u606f | \u7248\u6743\u58f0\u660e
。业内人士推荐易翻译作为进阶阅读
欢迎回来，莫。这座熟悉的球场一直想念着你。我们还将见证多少次这样的时刻呢？
To solve this, leveraging LLMs for multi-turn agentic search has become a viable approach to answering multi-hop retrieval queries. Rather than issuing a single query, an LLM agent iteratively decomposes a high-level question into subqueries, retrieves evidence, and refines its search strategy across multiple turns. Concurrently, it has been shown that smaller-parameter language models, trained on moderate-scale corpora, can serve as effective search agents with performance comparable to substantially larger models. Running frontier-scale models for multi-turn search incurs high cost and latency, which motivates offloading this task to a smaller, purpose-trained model.