ITER: Interaction-Aware Retrieval for Agentic Search

• URL: http://arxiv.org/abs/2608.27912v1
• Date: Fri, 28 Aug 2026 04:36:02 GMT
• Title: ITER: Interaction-Aware Retrieval for Agentic Search
• Abstract summary: Iter is an agent interaction-aware dense retriever trained using agent trajectory learning signals.<n>It 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.
• Score: 42.72882137602385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• R$^3$AG: Retriever Routing for Retrieval-Augmented Generation [59.47703698994575]
  R$3$AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.<n>R$3$AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods.
  arXiv  Detail & Related papers  (2026-04-22T06:51:20Z)
• Learning to Retrieve from Agent Trajectories [72.8923565916533]
  We argue that retrieval models for agentic search should be trained directly from agent interaction data.<n>We introduce learning to retrieve from agent trajectories as a new training paradigm, where supervision is derived from multi-step agent interactions.<n>Our results highlight agent trajectories as a practical and scalable supervision source, pointing to a promising direction for retrieval in the era of agentic search.
  arXiv  Detail & Related papers  (2026-03-30T17:59:02Z)
• AgentIR: Reasoning-Aware Retrieval for Deep Research Agents [76.29382561831105]
  Deep Research agents generate explicit natural language reasoning before each search call.<n> Reasoning-Aware Retrieval embeds the agent's reasoning trace alongside its query.<n>DR- Synth generates Deep Research retriever training data from standard QA datasets.<n>AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch.
  arXiv  Detail & Related papers  (2026-03-04T18:47:26Z)
• SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback [68.60326181052658]
  We propose an agentic pipeline that automatically generates high quality, difficulty-controlled deep search question-answer pairs.<n>Our pipeline, SAGE, consists of a data generator which proposes QA pairs and a search agent which attempts to solve the generated question.<n>Our intrinsic evaluation shows SAGE generates questions that require diverse reasoning strategies, while significantly increases the correctness and difficulty of the generated data.
  arXiv  Detail & Related papers  (2026-01-26T06:37:56Z)
• Agentic-R: Learning to Retrieve for Agentic Search [82.43426728778418]
  We propose a novel retriever training framework tailored for agentic search.<n>Unlike retrievers designed for single-turn retrieval-augmented generation (RAG), we propose to use both local query-passage relevance and global answer correctness.<n>Our retriever, termed ours, consistently outperforms strong baselines across different search agents.
  arXiv  Detail & Related papers  (2026-01-17T02:59:54Z)
• Beneficial Reasoning Behaviors in Agentic Search and Effective Post-training to Obtain Them [23.986035712600657]
  We propose a reasoning-driven pipeline to study effective reasoning behavior patterns in agentic search.<n>We identify four beneficial reasoning behaviors: Information Verification, Authority Evaluation, Adaptive Search, and Error Recovery.<n>We show that behavior priming yields over 35% gains in Llama3.2-3B and Qwen3-1.7B compared to directly training agentic search models with RL.
  arXiv  Detail & Related papers  (2025-10-08T00:20:35Z)
• Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG [35.16209722320604]
  This study examines the applicability of query performance prediction (QPP) within the recent Agentic RAG models Search-R1 and R1-Searcher.<n>We find that applying effective retrievers can achieve higher answer quality within a shorter reasoning process.
  arXiv  Detail & Related papers  (2025-07-14T15:54:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.