AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

ArXi:2604.05846v1 Announce Type: new Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat external information as unstructured text and fail to leverage the topological dependencies inherent in real-world data. To bridge this gap, we