Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

Summary

This survey proposes viewing agent evolution as dynamic graph transformation, modeling 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. The authors 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. They propose dynamic graph learning as reusable infrastructure for self-evolving agents, mapping nine dynamic-graph-learning subfields to agent-evolution capabilities and discussing adaptations and possible failure modes. Finally, they discuss five types of graph-aware evaluation and governance protocols from a dynamic-graph perspective. The goal is to provide a compact structural lens for designing and governing self-evolving agents.