RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems

2026-04-30

AI Summary

This study addresses the limitations of large language models in generating scientific research roadmaps—specifically, insufficient domain expertise, suboptimal task decomposition, and logical inconsistencies—by proposing RoadMapper, a multi-agent collaborative framework that structures the generation process into three phases: initial drafting, knowledge augmentation, and iterative critique-revision-evaluation. The work introduces RoadMap, the first benchmark dataset for research roadmap generation, and integrates knowledge enhancement with multi-agent coordination. Experimental results demonstrate that RoadMapper significantly outperforms baseline methods in domain specificity, logical coherence, and practical utility, achieving an average performance improvement of over 8% while reducing generation time to merely 16% of that required by human experts.

Abstract

People commonly leverage structured content to accelerate knowledge acquisition and research problem solving. Among these, roadmaps guide researchers through hierarchical subtasks to solve complex research problems step by step. Despite progress in structured content generation, the roadmap generation task has remained unexplored. To bridge this gap, we introduce RoadMap, a novel benchmark designed to evaluate the ability of large language models (LLMs) to construct high-quality roadmaps for solving complex research problems. Based on this, we identify three limitations of LLMs: (1) lack of professional knowledge, (2) unreasonable task decomposition, and (3) disordered logical relationships. To address these challenges, we propose RoadMapper, an LLM-based multi-agent system that decomposes the research roadmap generation task into three key stages (i.e., initial generation, knowledge augmentation, and iterative "critique-revise-evaluate"). Extensive experiments demonstrate that RoadMapper can improve LLMs' ability for roadmap generation, while enhancing average performance by more than 8% and saving 84% of the time required by human experts, highlighting its effectiveness and application potential.

Problem

• roadmap generation
• complex research problems
• large language models
• structured content
• task decomposition

Innovation

• roadmap generation
• multi-agent system
• large language models
• knowledge augmentation
• iterative critique-revise-evaluate