Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
New research conceptualizes self-evolving AI agents as dynamic graph structures to improve long-term system adaptability.
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:...
ArXiv CS.AI
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Framing agent evolution as dynamic graph transformation provides a rigorous structural framework for how AI systems learn, adapt, and refine their own operational logic over time. This approach shifts the focus from static tool-calling toward a cohesive architecture where memory, skills, and inter-agent relations evolve in tandem through formal topological updates.
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