ITER, an agent interaction-aware dense retriever trained using agent trajectory learning signals, is introduced andlations show that structured interaction history and pre-search reasoning provide complementary retrieval context, while previously visited and useful documents provide the strongest trajectory-relative su...
Hao-Dong Chen, Shuai Wang, Yu Yin et al.· 0 citations
This work introduces S IEVE, a search–inspect–fetch strategy built around a Boolean Query Language (BQL), which improves accuracy with every tested ranker, and the accuracy–context advantage persists across retriever choices and agent backbones.
Shuai Wang, Hao-Dong Chen, Yu Yin et al.· 0 citations
Deep-research agents answer complex user questions through an iterative sequence of search steps, where the agent autonomously formulates sub-queries to retrieve the evidence needed at each stage. However, existing retriever training typically relies only on the sub-query and its corresponding search results at the cur...
Hao-Dong Chen, Shuai Wang, Yu Yin et al.· 0 citations
This work introduces Sieve, a search-inspect-fetch strategy driven by a Boolean Query Language (BQL): it searches webpage fields to filter candidates, uses an interchangeable ranker to order them, presents structure-rich result cards for inspection, and fetches only selected sections.
Shuai Wang, Hao-Dong Chen, Yu Yin et al.· 4 citations
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