IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917434315767808 |
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| author | Ma, Fengbo Rao, Zixin Li, Xiaoting Chen, Zhetao Sun, Hongyue Zhao, Yiping Chen, Xianyan Xiang, Zhen |
| author_facet | Ma, Fengbo Rao, Zixin Li, Xiaoting Chen, Zhetao Sun, Hongyue Zhao, Yiping Chen, Xianyan Xiang, Zhen |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22861 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review Ma, Fengbo Rao, Zixin Li, Xiaoting Chen, Zhetao Sun, Hongyue Zhao, Yiping Chen, Xianyan Xiang, Zhen Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.22861 |