论文概要
研究领域: NLP 作者: Komal Kumar, Aaman Chadha, Salman Khan 发布时间: 2025-04-08 arXiv: 2504.06264
中文摘要
科学文献的快速增长使得研究人员越来越难以高效地发现、评估和综合相关研究工作。多智能体大语言模型（LLM）的最新进展在理解用户意图和利用各种工具方面展现出巨大潜力。本文介绍 Paper Circle——一个多智能体研究发现与分析系统，旨在减少查找、评估、组织和理解学术文献所需的工作量。该系统包含两个互补的流水线：（1）发现流水线，集成离线和在线多源检索、多标准评分、多样性感知排序和结构化输出；（2）分析流水线，将单篇论文转换为结构化知识图谱，包含概念、方法、实验和图表等类型化节点，支持图谱感知问答和覆盖验证。两个流水线均在基于编码器LLM的多智能体编排框架中实现，并在每个智能体步骤生成完全可复现、同步的输出（JSON、CSV、BibTeX、Markdown和HTML）。
原文摘要
The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated strong potential for understanding user intent and are being trained to utilize various tools. In this paper, we introduce Paper Circle, a multi-agent research discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature. The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-criteria scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual ...
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