LLMs are evolving rapidly, with newer models offering stronger capabilities. This suggests that in LLM-based relevance judging, more capable models will achieve higher agreement with human judgements under the same prompt. We challenge this understanding by investigating the behavior of LLM-based relevance judges under...
Chu-Chun Yu, Guido Zuccon, T. Leelanupab· 0 citations
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
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
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