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 https://papercircle.vercel.app/ and the code at https://github.com/MAXNORM8650/papercircle.

翻译：科学文献的快速增长使得研究人员高效发现、评估和综合相关工作的难度与日俱增。近年来，多智能体大语言模型（LLMs）在理解用户意图方面展现出强大潜力，并被训练用于调用各类工具。本文介绍论文圈（Paper Circle）——一个旨在降低查找、评估、整理和理解学术文献工作量的多智能体科研发现与分析系统。该系统包含两条互补管线：（1）发现管线：集成多源离线与在线检索、多准则评分、多样性感知排序及结构化输出；（2）分析管线：将单篇论文转化为带类型节点（如概念、方法、实验、图表）的结构化知识图谱，支持图谱感知问答与覆盖度验证。两条管线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包括JSON、CSV、BibTeX、Markdown和HTML在内的完全可复现、可同步的输出。本文详细阐述了构成论文圈研究流程的系统架构、智能体角色、检索与评分方法、知识图谱模式及评估接口。我们在论文检索与论文综述生成两个任务上对论文圈进行基准测试，报告了命中率、平均倒数排名（MRR）及Top-K召回率，结果显示更强的智能体模型能带来持续的性能提升。我们已在https://papercircle.vercel.app/公开发布网站，在https://github.com/MAXNORM8650/papercircle公开代码。