ITER: Interaction-Aware Retrieval for Agentic Search
Information Retrieval
Summary
The authors present iter, a new method for training agents that answer complex questions by breaking them into smaller searches. Unlike previous methods that only use the current search step for training, iter also uses the main question and earlier search steps to better understand the query. This approach helps iter perform better across different agent types and tasks. Their tests show iter is more accurate and reliable than earlier retrievers like LRAT and AgentIR.
Authors
Haodong Chen, Shuai Wang, Yu Yin, Shengyao Zhuang, Guido Zuccon, Teerapong Leelanupab
Abstract
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 current step as training signals, leaving the information accumulated from previous interactions largely underutilized. We introduce iter, an agent interaction-aware dense retriever trained using agent trajectory learning signals. iter represents each query by incorporating not only the current sub-query, but also the main question and preceding sub-queries, and is trained using trajectory-relative learning signals derived from the agent's interactions. Across six agent backbones from three model families, iter consistently outperforms the existing agent-trajectory-trained dense retriever, LRAT, achieving an average improvement of 7.5% on InfoSeek-Eval and 13.5% on BrowseComp-Plus. iter also demonstrates stronger cross-agent robustness than AgentIR, a deep-research retriever that relies on external LLM-judge signals and the agent's pre-search reasoning. Ablations further show that the main question and previous sub-queries provide the most robust query representation, while previously visited and useful documents, used as redundancy negatives in subsequent searches, provide the strongest trajectory-relative supervision. Code is available at https://github.com/ielab/ITER.