RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems
This new AI system outperforms humans at breaking down complex research problems…
Structured content like roadmaps helps researchers navigate complex problems by breaking them into hierarchical subtasks. Yet generating such roadmaps with LLMs remains unexplored. To fill this gap, researchers present RoadMap, a new benchmark for evaluating how well LLMs construct research roadmaps. The benchmark reveals three core LLM weaknesses: insufficient professional knowledge, illogical task decomposition, and disordered relationships between steps.
To address these, the team built RoadMapper, a multi-agent system that decomposes roadmap generation into three stages: initial generation, knowledge augmentation, and an iterative 'critique-revise-evaluate' loop. Experiments show RoadMapper delivers over 8% average performance gains compared to baseline LLMs and slashes the time required by human experts by 84%. Accepted to Findings of ACL 2026, this work demonstrates a viable path toward automating research planning and accelerating scientific discovery.
- Introduces RoadMap, a novel benchmark for evaluating LLMs on research roadmap generation tasks
- RoadMapper uses three stages—initial generation, knowledge augmentation, and iterative critique-revise-evaluate—to overcome LLM limitations
- Achieves 8%+ performance improvement over baselines and reduces expert time by 84%
Why It Matters
Automating research roadmaps could accelerate scientific discovery and help researchers navigate complex problems faster.