PaperReview: An AI-Driven Academic Research Platform with Multi-Agent Collaboration for Paper Review and Knowledge Graph System

PaperReview is an AI-driven academic research platform developed by KimJiSeong1994, designed to address the problem of academic information overload. The platform integrates four core functions: intelligent paper retrieval, multi-agent review, knowledge graph construction, and personalized research workflows, providing researchers with all-round academic assistance from literature discovery to knowledge organization.

In fields like AI and computer science, the output of academic papers is growing exponentially (e.g., dozens of new papers are published daily in the machine learning field on arXiv). Researchers face three major challenges: discovering relevant research in a timely manner, quickly understanding the core contributions of papers, and establishing connections in domain knowledge. Traditional manual retrieval and reading methods are no longer sufficient, and intelligent tools are urgently needed for assistance.

PaperReview's core functions include:

1. Intelligent Paper Retrieval: Supports semantic search, multi-source aggregation (arXiv/PubMed, etc.), intelligent recommendation, and trend analysis;
2. Multi-Agent Review: Generates structured review reports through four agents: contribution identification, method analysis, related work comparison, and quality evaluation;
3. Knowledge Graph Construction: Extracts entities and relationships, tracks research evolution, and provides visual exploration;
4. Personalized Workflow: Supports reading list management, note annotation, team collaboration, and one-click citation export.

PaperReview's technical architecture includes:

• Multi-Agent System: Coordinator-executor architecture, where specialized agents handle specific review dimensions and support memory sharing;
• Knowledge Graph Technology: Uses NER/relationship extraction to extract information, stores data in a graph database, represents entity relationships with vectorization, and supports incremental updates;
• Retrieval-Augmented Generation (RAG): Combines retrieved content to improve generation quality and relevance.

PaperReview is suitable for multiple scenarios:

1. Literature Review Writing: Quickly collect literature, generate summaries, and understand research context through knowledge graphs;
2. Research Direction Exploration: Use knowledge graphs to discover domain gaps and potential opportunities;
3. Paper Writing Assistance: Find related work, generate citation formats, and check term consistency;
4. Academic Social Interaction: Follow authors/topics and get personalized academic information streams.

PaperReview has obvious advantages over traditional tools and simple AI summary tools:

Function	Traditional Literature Management	Simple AI Summary	PaperReview
Paper Retrieval	Basic	Partially supported	Semantic + multi-source
Content Understanding	Manual	Single-dimensional	Multi-agent
Knowledge Connection	Tags	None	Knowledge graph
Personalization	Limited	None	Workflow customization
Collaboration	Limited	None	Team sharing

Usage Value: Saves time for researchers (automated retrieval and analysis), improves research quality (multi-agent review), expands horizons (knowledge graphs), and enhances collaboration (team sharing). Future Directions: Support more disciplines, integrate experimental design and code reproduction, provide writing assistance and polishing, and establish an academic influence evaluation model.

PaperReview represents a new direction in AI-assisted academic research. By combining multi-agent collaboration, knowledge graphs, and personalized workflows, it provides researchers with powerful tools. In the era of information overload, such intelligent tools will become indispensable assistants for researchers, helping them efficiently discover, understand, and create knowledge.