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

arXiv cs.CL / 5/1/2026

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Key Points

The paper introduces RoadMap, a new benchmark for evaluating how well large language models (LLMs) can generate high-quality roadmaps for complex research problems.
It diagnoses three key failure modes of current LLMs for this task: insufficient professional knowledge, poor task decomposition, and illogical or disordered relationships between steps.
To overcome these issues, the authors propose RoadMapper, an LLM-based multi-agent system that generates roadmaps through three stages: initial generation, knowledge augmentation, and an iterative critique–revise–evaluate loop.
Experiments show RoadMapper improves roadmap-generation performance by more than 8% on average and reduces the time needed compared with human experts, claiming a 84% time saving.

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.