Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2510.09156v1
• Date: Fri, 10 Oct 2025 09:00:07 GMT
• Title: Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning
• Authors: Jing Li, Zhijie Sun, Zhicheng Zhou, Suming Qiu, Junjie Huang, Haijia Sun, Linyuan Qiu,
• Abstract summary: Agentic-KGR is a novel framework enabling co-evolution between large language models (LLMs) and knowledge graphs (KGs)<n>Our approach introduces three key innovations: (1) a dynamic schema expansion mechanism that systematically extends graph beyond pre-defined boundaries during training; (2) a retrieval-augmented memory system enabling synergistic co-evolution between model parameters and knowledge structures through continuous optimization; and (3) a learnable multi-scale prompt compression approach that preserves critical information while reducing computational complexity through adaptive sequence optimization.
• Score: 6.665920297143511
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Current knowledge-enhanced large language models (LLMs) rely on static, pre-constructed knowledge bases that suffer from coverage gaps and temporal obsolescence, limiting their effectiveness in dynamic information environments. We present Agentic-KGR, a novel framework enabling co-evolution between LLMs and knowledge graphs (KGs) through multi-round reinforcement learning (RL). Our approach introduces three key innovations: (1) a dynamic schema expansion mechanism that systematically extends graph ontologies beyond pre-defined boundaries during training; (2) a retrieval-augmented memory system enabling synergistic co-evolution between model parameters and knowledge structures through continuous optimization; (3) a learnable multi-scale prompt compression approach that preserves critical information while reducing computational complexity through adaptive sequence optimization. Experimental results demonstrate substantial improvements over supervised baselines and single-round RL approaches in knowledge extraction tasks. When integrated with GraphRAG, our method achieves superior performance in downstream QA tasks, with significant gains in both accuracy and knowledge coverage compared to existing methods.

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