ITER: Interaction-Aware Retrieval for Agentic Search

2026-08-28

AI Summary

为解决深度研究代理在多步骤搜索中信息利用不足的问题，提出了一种基于交互轨迹学习信号的密集检索器ITER，通过结合主问题和先前子查询提高搜索效果。

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.

Problem

• retriever training
• sub-query
• interaction-aware
• trajectory learning
• information accumulation

Innovation

• Interaction-Aware Retrieval
• Agent Trajectory Learning Signals
• Cross-Agent Robustness
• Dense Retriever