让大模型像人一样精读文献，精准回答科研问题。
IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review
- 模仿人类阅读习惯，分两阶段提取文献关键信息
- 在跨领域测试中比现有方法高13.2%准确率
- 适合需要深度文献分析的科研人员使用
科学研究依赖于从文献中准确获取信息以支持分析决策。本文提出一项新任务——通过文献综述进行信息检索（IntraView），旨在针对研究驱动型问题，自动化地从给定内容中精准提取细粒度信息。为此，我们提出IntrAgent，一个基于大语言模型的智能体，模拟人类在文献检索中的行为：识别相关段落，并通过迭代式阅读持续提取细节，逐步完善答案。该系统采用双阶段流程：第一阶段为段落排序，利用结构化知识推理优先筛选相关部分；第二阶段为迭代阅读，不断提取信息并合成简洁、上下文相关的回答。为支持严格评估，我们构建了IntraBench基准，包含315个测试实例，涵盖五个STEM领域，由专家撰写问题与文献配对而成。在七种主流大模型上，IntrAgent平均比现有SOTA的RAG和研究代理基线高出13.2%的跨领域准确率。
原文摘要 · Abstract (English)
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.
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