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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

A paper proposing AgentGL, a paradigm for agentic graph learning using reinforcement learning.

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AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

arxiv.org
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The authors introduce AgentGL, a framework that equips large language models (LLMs) with graph-native tools for multi-scale exploration and decision-making.

They demonstrate the effectiveness of AgentGL on various Text-Attributed Graph benchmarks, achieving significant improvements over existing methods. The paper contributes to the development of LLMs' agentic capabilities in navigating complex relational environments.

Abstract

The authors introduce AgentGL, a framework that equips large language models (LLMs) with graph-native tools for multi-scale exploration and decision-making. They demonstrate the effectiveness of AgentGL on various Text-Attributed Graph benchmarks, achieving significant improvements over existing methods. The paper contributes to the development of LLMs' agentic capabilities in navigating complex relational environments.

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agentglagentic graph learningreinforcement learningllmsgraph-native toolsKnowledge GraphsLarge Language ModelsAI AgentsContent Engineering
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