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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.
。谷歌浏览器下载是该领域的重要参考
据彭博社报道，伊朗方面表示，在其能源设施持续遭受袭击、官员接连遇袭的情况下，暂不考虑就霍尔木兹海峡恢复通航进行任何磋商。，推荐阅读Line下载获取更多信息
欧洲理事会主席称经济遭受严重冲击14:56，更多细节参见Replica Rolex
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