Abstract
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 papers into structured knowledge graphs with typed nodes such as concepts, methods, experiments, and figures, enabling graph-aware question answering and coverage verification. Both pipelines are implemented within a coder LLM-based multi-agent orchestration framework and produce fully reproducible, synchronized outputs including JSON, CSV, BibTeX, Markdown, and HTML at each agent step. This paper describes the system architecture, agent roles, retrieval and scoring methods, knowledge graph schema, and evaluation interfaces that together form the Paper Circle research workflow. We benchmark Paper Circle on both paper retrieval and paper review generation, reporting hit rate, MRR, and Recall at K. Results show consistent improvements with stronger agent models. We have publicly released the website at this https URL and the code at this https URL.
Abstract (translated)
科学文献的快速增长使研究人员高效发现、评估和综合相关研究的工作变得日益困难。近期多智能体大语言模型（LLMs）的进展已展现出理解用户意图的强劲潜力，且这些模型正被训练以利用各类工具。本文介绍了Paper Circle——一个旨在减少查找、评估、组织和理解学术文献所需工作量的多智能体研究探索与分析系统。该系统包含两个互补的流程：（1）探索流程，整合了多源离线与在线检索、多标准评分、注重多样性的排序及结构化输出；（2）分析流程，将单篇论文转化为包含概念、方法、实验和图表等类型化节点的结构化知识图谱，从而实现图感知问答与覆盖度验证。两个流程均基于编码器大语言模型的多智能体编排框架实现，并在每个智能体步骤生成完全可复现的同步输出，包括JSON、CSV、BibTeX、Markdown和HTML格式。本文详细描述了系统架构、智能体角色、检索与评分方法、知识图谱模式以及评估界面，这些共同构成了Paper Circle的研究工作流。我们在论文检索和论文综述生成两个任务上对Paper Circle进行了基准测试，报告了命中率、平均倒数排序（MRR）及K值召回率。结果表明，使用更强大的智能体模型可带来持续的性能提升。我们已公开该系统网站（此https链接）及代码仓库（此https链接）。
URL
https://arxiv.org/abs/2604.06170
