Agentic publications: redesigning scientific publishing in the age of thinking large language models

Abstract

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.