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.