HUMAN-AI CO-CREATION SYSTEM FOR KNOWLEDGE WORK BASED ON MULTI-AGENT APPROACH

The task of writing a scientific research proposal is complex and highly structured, yet traditional writing methods are typically inefficient. To address this challenge, this study introduces an intelligent writing system leveraging a large language model (LLM). The core contribution is a modular proposal drafting framework that automatically generates multi-chapter application content based on user-specified disciplines and topic questions. The system first creates outlines and content overviews for each chapter through intent recognition, then composes detailed chapter content guided by these outlines. Finally, the system consolidates these components into a complete proposal draft. This modular architecture not only ensures logical consistency across the document but also empowers users to independently refine and optimize individual chapters. To evaluate the system's efficacy, we invited multiple researchers for assessment. Benchmarking against a single-agent LLM demonstrates that our multi-agent system produces proposals with superior content coverage, logical coherence, and user satisfaction, while significantly improving writing efficiency. The proposed modular prompt framework exhibits broader applicability and can be readily extended to other funding application contexts, offering a novel technological approach for advancing intelligent research support systems.