IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review

• URL: http://arxiv.org/abs/2604.22861v1
• Date: Thu, 23 Apr 2026 01:55:08 GMT
• Title: IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
• Abstract summary: IntrAgent is designed to mimic human behaviors when reading literature for information retrieval.<n>It identifies relevant sections and then iteratively extracting key details to refine the retrieved information.<n>IntrAgent achieves 13.2% higher cross-domain accuracy than state-of-the-art RAG and research-agent baselines.
• Score: 10.586701795115609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific research relies on accurate information retrieval from literature to support analytical decisions. In this work, we introduce a new task, INformation reTRieval through literAture reVIEW (IntraView), which aims to automate fine-grained information retrieval faithfully grounded in the provided content in response to research-driven queries, and propose IntrAgent, an LLM-based agent that addresses this challenging task. In particular, IntrAgent is designed to mimic human behaviors when reading literature for information retrieval -- identifying relevant sections and then iteratively extracting key details to refine the retrieved information. It follows a two-stage pipeline: a Section Ranking stage that prioritizes relevant literature sections through structural-knowledge-enabled reasoning, and an Iterative Reading stage that continuously extracts details and synthesizes them into concise, contextually grounded answers. To support rigorous evaluation, we introduce IntraBench, a new benchmark consisting of 315 test instances built from expert-authored questions paired with literature spanning five STEM domains. Across seven backbone LLMs, IntrAgent achieves on average 13.2% higher cross-domain accuracy than state-of-the-art RAG and research-agent baselines.

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