论文圈：开源多智能体研究发现与分析框架

Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

April 7, 2026

作者: Komal Kumar, Aman Chadha, Salman Khan, Fahad Shahbaz Khan, Hisham Cholakkal

cs.AI

摘要

科学文献的快速增长使得研究人员难以高效地发现、评估与整合相关研究成果。多智能体大语言模型的最新进展在理解用户意图方面展现出强大潜力，并正被训练以运用多种工具。本文提出Paper Circle——一个旨在降低学术文献查找、评估、整理及理解成本的多智能体研究发现与分析系统。该系统包含两条互补的流程链：（1）发现流程链，整合多源离线与在线检索、多标准评分、多样性感知排序及结构化输出；（2）分析流程链，将单篇论文转化为包含概念、方法、实验、图表等类型化节点的结构化知识图谱，支持基于图谱的智能问答与覆盖度验证。两条流程链均基于编码器LLM的多智能体协同框架实现，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）。本文详细阐述了系统架构、智能体角色、检索与评分方法、知识图谱模式及评估界面，这些要素共同构成了Paper Circle的研究工作流。我们在文献检索和论文综述生成任务上对系统进行基准测试，汇报了命中率、平均倒数排名和K值召回率等指标。实验结果表明，采用更强智能体模型能带来持续性能提升。我们已公开系统网站（https://papercircle.vercel.app/）与源代码（https://github.com/MAXNORM8650/papercircle）。

English

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.