[2605.06671] GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
[Submitted on 18 Apr 2026]
Title:GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
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Abstract:Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is still unsatisfying, since graphs are naturally more complex in topology and often require systematic multi-step reasoning, especially on larger graphs. Motivated by this gap, we propose GraphDC, a Divide-and-Conquer multi-agent framework for scalable graph algorithm reasoning. Specifically, inspired by Divide-and-Conquer design, GraphDC decomposes an input graph into smaller subgraphs, assigns each subgraph to a specialized agent for local reasoning, and uses a master agent to integrate the local outputs with inter-subgraph information to produce the final solution. This hierarchical design reduces the reasoning burden on individual agents, alleviates computational bottlenecks, and improves robustness on large graph instances. Extensive experiments show that GraphDC consistently outperforms existing methods on graph algorithm reasoning across diverse tasks and scales, especially on larger instances where direct end-to-end reasoning is less reliable.
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.06671 [cs.AI]
(or arXiv:2605.06671v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.06671
arXiv-issued DOI via DataCite
Submission history
From: Jiaming Cui [view email] [v1] Sat, 18 Apr 2026 22:41:29 UTC (145 KB)
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