@inproceedings{ma-etal-2026-intragent,
    title = "{I}ntr{A}gent: An {LLM} Agent for Content-Grounded Information Retrieval through Literature Review",
    author = "Ma, Fengbo  and
      Rao, Zixin  and
      Li, Xiaoting  and
      Chen, Zhetao  and
      Sun, Hongyue  and
      Zhao, Yiping  and
      Chen, Xianyan  and
      Xiang, Zhen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.29/",
    pages = "674--715",
    ISBN = "979-8-89176-390-6",
    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."
}