Summary of Scalable and Accurate Graph Reasoning with Llm-based Multi-agents, by Yuwei Hu et al.
Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents
by Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, Yongchao Liu
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces GraphAgent-Reasoner, a novel approach for complex graph reasoning tasks that utilizes a multi-agent collaboration strategy to explicitly reason about graphs. The framework is designed to overcome the limitations of Large Language Models (LLMs) in handling long text and graph structures. By decomposing graph problems into smaller node-centric tasks distributed among multiple agents, GraphAgent-Reasoner significantly reduces the complexity handled by a single LLM, enhancing its accuracy. The framework can efficiently scale to accommodate larger graphs with over 1,000 nodes and demonstrates near-perfect accuracy on polynomial-time graph reasoning tasks, outperforming state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphAgent-Reasoner is a new way for computers to solve complex problems about relationships between things. It’s like having many small helpers working together to figure something out. This helps the computer understand graphs (like networks of people or websites) much better than before. The more helpers you add, the bigger the graph can be and still get an accurate answer. This is important because it means we can use computers to do things like decide how important a website is based on its connections. |
