让大模型与知识图谱共同进化，动态更新知识
Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning
- 用多轮强化学习让大模型和知识图谱互相优化
- 在问答任务中准确率和知识覆盖均显著提升
- 适合需要实时更新知识的智能系统研发
当前增强知识的大语言模型依赖静态预构建的知识库，存在覆盖不足和时效过时问题，限制了其在动态信息环境中的效果。我们提出Agentic-KGR框架，通过多轮强化学习实现大模型与知识图谱（KG）的协同演化。方法包含三项创新：(1) 动态模式扩展机制，在训练过程中系统性地突破预设边界扩展图谱本体；(2) 检索增强的记忆系统，通过持续优化实现模型参数与知识结构的协同演进；(3) 可学习的多尺度提示压缩方法，通过自适应序列优化在保留关键信息的同时降低计算复杂度。实验表明，该方法在知识抽取任务中显著优于监督基线和单轮强化学习方法。与GraphRAG集成后，在下游问答任务中表现更优，准确率与知识覆盖率均有显著提升。
原文摘要 · Abstract (English)
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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