myWiki/raw/papers/ma-intragent-2026.md

title, type, arxiv, year, authors, venue, code, dataset, project

title	type	arxiv	year	authors	venue	code	dataset	project
IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review	raw-paper	2604.22861	2026	Fengbo Ma, Zixin Rao, Xiaoting Li, Zhetao Chen, Hongyue Sun, Yiping Zhao, Xianyan Chen, Zhen Xiang	arXiv 2026	https://github.com/FengboMa/IntrAgent	https://huggingface.co/datasets/IntrAgent/IntraBench	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).