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
Overall, LightMem offers a context-efficiency trade-off rather than a general advantage over Naive RAG, whose value depends on the retriever and available token budget, motivating future work on retrieval, reranking, query formulation, and their interaction with raw and constructed memory representations.
Yong Zhou, Shuai Wang, B. Koopman et al.· arXiv.org· 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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