用AI让论文变会思考的智能知识系统，解决文献爆炸难题。
Agentic publications: redesigning scientific publishing in the age of thinking large language models
- 构建多代理验证+检索增强生成的交互式论文框架
- 支持多语言、可动态更新、按需定制知识深度
- 适合跨学科研究者与需要高效获取知识的团队
本文提出“智能出版”（Agentic Publication）概念，一种由大语言模型驱动的新框架，旨在应对科学文献指数级增长带来的挑战。该框架通过检索增强生成与多代理验证，将结构化数据（知识图谱、元数据）与非结构化内容（文本、多媒体）融合，形成可交互的知识系统。系统提供面向人类和人工智能代理的接口，既支持叙事性解释，也输出机器可读结果。实现依赖向量数据库进行语义搜索，知识图谱支持结构化推理，多代理协同验证保障可靠性。原型演示展示了多语言交互、API可访问性、持续知识流动和结构化表征能力。系统支持知识动态更新、新发现整合与个性化细节呈现。该模式在保持科学严谨性的前提下，实现响应式知识合成，结合传统出版路径，构建更高效、开放、协作的研究生态，尤其适用于跨学科领域。实际应用中，可为研究人员提供跨领域精准信息获取，并通过自动化验证、专家监督与透明治理机制应对伦理风险。
原文摘要 · Abstract (English)
Purpose: This paper introduces the concept of "Agentic Publication," a novel LLM-driven framework designed to complement traditional scientific publishing by transforming papers into interactive knowledge systems that address challenges created by exponential growth in scientific literature. Design/methodology/approach: Our architecture integrates structured data (knowledge graphs, metadata) with unstructured content (text, multimedia) through retrieval-augmented generation and multi-agent verification. The system provides interfaces for humans and artificial agents, offering narrative explanations alongside machine-readable outputs. Implementation leverages vector databases for semantic search, knowledge graphs for structured reasoning, and collaborative verification agents. Findings: Our proof-of-concept demonstration showcases multilingual interaction, API accessibility, continuous knowledge flow, and structured knowledge representation. The framework enables dynamic updating of knowledge, synthesis of new findings, and customizable detail levels. Originality: The Agentic Publication represents a transformative approach to scientific communication by creating responsive knowledge synthesis systems while maintaining scientific rigor. Integrating multi-agent verification with traditional publishing pathways creates a more efficient, accessible, and collaborative research ecosystem, particularly valuable in interdisciplinary fields. Practical implications: The system is a powerful companion for researchers navigating complex knowledge landscapes, offering tailored information access across disciplines while addressing ethical considerations through automated validation, expert oversight, and transparent governance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。