论文圈：一个开源的多智能体研究探索与分析框架

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

科學文獻的快速增長使得研究人員愈發難以高效地發現、評估與整合相關研究成果。近期多智能體大語言模型（LLMs）的進展展現出理解用戶意圖的強大潛力，並正被訓練以運用多種工具。本文介紹Paper Circle——一個旨在降低學術文獻發現、評估、組織與理解所需精力的多智能體研究發現與分析系統。該系統包含兩個互補的流程：（1）發現流程：整合多源離線與在線檢索，具備多標準評分、多樣性感知排序及結構化輸出功能；（2）分析流程：將單篇論文轉化為包含概念、方法、實驗、圖表等類型化節點的結構化知識圖譜，支持圖感知問答與覆蓋度驗證。兩大流程均基於編碼器LLM的多智能體協調框架實現，並在每個智能體步驟生成完全可復現的同步輸出（包括JSON、CSV、BibTeX、Markdown及HTML格式）。本文詳細闡述了系統架構、智能體角色、檢索與評分方法、知識圖譜模式及評估界面，共同構成Paper Circle的研究工作流。我們在文獻檢索與論文綜述生成任務上對Paper Circle進行基準測試，報告了命中率、平均倒數排名及K值召回率。結果表明，採用更強智能體模型能持續提升性能。我們已公開系統網站（https://papercircle.vercel.app/）與代碼庫（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.