Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
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 a...
| Authors | Yuanyuan Xu, Wenjie Zhang, Yin Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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