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+---
+title: "IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review"
+type: raw-paper
+arxiv: "2604.22861"
+year: 2026
+authors: "Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen, Zhen Xiang"
+venue: "arXiv 2026"
+code: "https://github.com/FengboMa/IntrAgent"
+dataset: "https://huggingface.co/datasets/IntrAgent/IntraBench"
+project: "https://intragent.github.io/"
+---
+
+# IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
+
+**Authors:** Fengbo Ma*, Zixin Rao*, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen†, Zhen Xiang†
+**Affiliation:** University of Georgia, Athens, GA, USA
+**arXiv:** 2604.22861
+**Date:** April 23, 2026
+
+## 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.
+
+## Key Contributions
+
+1. **IntraView Task** — A novel task for accurate, automated, and content-grounded information retrieval from a provided scientific literature.
+2. **IntrAgent Framework** — An LLM agent with a two-stage pipeline (Section Ranking + Iterative Reading) that mimics human reading behavior.
+3. **Hierarchy Preservation** — Leverages structural knowledge of scientific documents for more effective section ranking.
+4. **Sufficiency Check** — Mitigates hallucination by explicitly assessing whether accumulated information is adequate to answer the query.
+5. **IntraBench** — The first benchmark for evaluating IntraView, with 315 test instances across five domains (physics, earth science, public health, engineering, material science).
+
+## Method Overview
+
+### Section Ranking
+1. **Section Heading Parsing**: Convert literature to Markdown with minerU for layout/section detection.
+2. **Hierarchy Preservation**: Construct a section tree from headings using LLM-based hierarchy inference.
+3. **Reasoning-Based Ranking**: LLM ranks sections by relevance to the research question via structure-aware reasoning.
+
+### Iterative Reading
+- **Reordered Section Access**: Read sections in descending relevance order.
+- **Section Detail Extraction**: Extract key scientific details (terminology, numbers, experiments, statistics, conclusions).
+- **Information Sufficiency Check**: LLM evaluates whether accumulated details are sufficient; terminates or continues reading.
+- **Confidence-Based Reading Styles**: Conservative, balanced (default), and aggressive modes to control operational overhead.
+- **Final Answer Synthesis**: Synthesize answer from all accumulated details.
+
+## Evaluation
+
+- **IntraBench**: 315 test instances across physics, earth science, public health, engineering, material science.
+- **LLM-Grounded Multiple-Choice Evaluation**: LLM maps generated free-form answers to multiple-choice candidates, addressing synonym/abbreviation challenges.
+- **Baselines**: RAG systems (vanilla RAG, re-ranking, contextual retrieval) and literature agents (PaperQA2, QASA, SciMaster).
+- **Results**: 13.2% average cross-domain accuracy improvement over baselines across 7 backbone LLMs.
+
+## Key Design Insights
+
+- Structural knowledge (section hierarchy) is critical for accurate section ranking — semantic similarity alone insufficient.
+- Sufficiency check prevents both hallucination (premature answer with insufficient evidence) and over-reading.
+- The framework can handle queries where the answer is NOT present in the literature (through explicit "None of the above" handling).