发表机构
School of Computer Science and Engineering, The University of New South Wales; Faculty of Engineering and Information Technology, University of Technology Sydney; School of Data Science, The Chinese University of Hong Kong-Shenzhen; Zhejiang Gongshang University（新南威尔士大学计算机科学与工程学院; 悉尼科技大学工程与信息技术学院; 香港中文大学（深圳）数据科学学院; 浙江工商大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述将自进化智能体建模为动态图变换，梳理相关动态图方法分类，提出动态图学习作为智能体可复用基础设施，探讨图感知评估协议，为自进化智能体设计治理提供结构性视角。
AI 中文摘要
基于大语言模型（LLM）的智能体正日益成为在交互过程中持续存在的自进化系统，具备记忆存储、工具使用、技能获取、工作流优化及与其他智能体协同的能力。这些能力使智能体状态呈现结构性与动态性：实体、关系、属性、依赖及执行结构会随新证据、反馈和环境条件发生变化。现有图-智能体综述通常将图视为智能体功能的支撑结构，而非进化基底；而自进化智能体综述则聚焦智能体层面机制，极少探讨图拓扑的进化过程，因此进化智能体状态与动态图拓扑间的耦合关系仍未得到充分探索。本综述通过将智能体进化定义为动态图变换，将上述两条研究脉络关联起来：我们将智能体状态建模为动态图，其中记忆、工具、技能、工作流及智能体间关系被表示为类型化节点、边和子图，通过受模式约束的重写进行更新。基于该建模，我们将现有面向自进化智能体的动态图方法整理为四类：节点/特征进化、边/拓扑进化、子图激活及跨组件协同进化。在此分类基础上，我们提出动态图学习作为自进化智能体的可复用基础设施，并将9个动态图学习子领域映射到智能体进化能力，讨论其适配方式及可能的失效模式。最后，我们从动态图视角探讨了五类图感知的评估与治理协议，以补充端任务评估，旨在为自进化智能体的设计与治理提供紧凑的结构性视角。
英文摘要
Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories, use tools, acquire skills, refine workflows, and coordinate with other agents. These capabilities make agent states structural and dynamic: entities, relations, attributes, dependencies, and execution structures change with new evidence, feedback, and environmental conditions. Existing graph-agent surveys typically treat graphs as support structures for agent functions rather than as evolving substrates, while self-evolving-agent surveys focus on agent-level mechanisms and rarely discuss graph topology evolution. Thus, the coupling between evolving agent state and dynamic graph topology remains underexplored. This survey connects these two research lines by framing \textit{agent evolution as dynamic graph transformation}. We model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites. Based on this formulation, we organize existing dynamic-graph-based methods for self-evolving agents into four taxonomies: node/feature evolution, edge/topology evolution, subgraph activation, and cross-component co-evolution. Building on this taxonomy, we propose dynamic graph learning as reusable infrastructure for self-evolving agents and map nine dynamic-graph-learning subfields to agent-evolution capabilities, discussing their adaptations and possible failure modes. Finally, we discuss five types of graph-aware evaluation and governance protocols from a dynamic-graph perspective, which complement end-task evaluation. The goal is to provide a compact structural lens for designing and governing self-evolving agents.
CommentsProject: https://github.com/LuckyGirl-XU/Awesome-Agent-Dynamic-Graphs.git
