GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning

2026-05-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

GraphDC is a novel multi-agent framework designed to enhance the scalability and accuracy of Large Language Models (LLMs) in solving complex graph algorithm reasoning tasks. It addresses the limitations of single LLMs, which struggle with larger, denser graphs requiring multi-step reasoning. Inspired by the divide-and-conquer paradigm, GraphDC decomposes an input graph into smaller subgraphs, assigning each to a specialized agent for local reasoning. A master agent then integrates these local outputs with inter-subgraph information to produce a final solution. This hierarchical approach reduces the reasoning burden on individual agents, alleviates computational bottlenecks, and improves robustness. Experimental results demonstrate that GraphDC consistently outperforms existing LLM-based methods, particularly on larger graphs (e.g., 80-100 nodes) and challenging tasks like shortest path and cycle detection, where it achieves nearly twofold accuracy gains.

Key takeaway

For research scientists developing LLM-based graph reasoning systems, GraphDC demonstrates that a divide-and-conquer multi-agent architecture significantly improves performance and scalability on large, complex graphs. You should consider implementing subgraph-level decomposition and hierarchical reasoning to overcome the inherent limitations of single LLMs on such tasks, especially for applications requiring robust performance on graphs with 60+ nodes.

Key insights

GraphDC uses a divide-and-conquer multi-agent system to scale LLM-based graph reasoning by decomposing complex graphs.

Principles

• Decompose complex problems into manageable subproblems.
• Distribute reasoning load across specialized agents.
• Integrate local solutions with global context.

Method

GraphDC first partitions a graph into subgraphs and generates sub-queries. Sub-agents perform local reasoning on their assigned subgraphs, and an extractor distills responses. A master agent then synthesizes these sub-answers with inter-subgraph dependencies for the final solution.

In practice

• Apply graph decomposition for large-scale graph problems.
• Use multi-agent systems to distribute LLM reasoning tasks.
• Prioritize inter-subgraph information for global consistency.

Topics

• Graph Algorithm Reasoning
• Large Language Models
• Multi-Agent Systems
• Divide-and-Conquer Paradigm
• Graph Decomposition

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.