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arXiv:2608.21156v1 Announce Type: cross Abstract: LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms includi...
The paper surveys self‑evolving agents, highlighting that their states—memories, tools, skills, workflows, and inter‑agent relations—are dynamic and can be modeled as evolving graphs. It critiques existing surveys for treating graphs merely as support structures and proposes a framework that views agent evolution as dynamic graph transformation, categorizing methods into node/feature, edge/topology, subgraph activation, and cross‑component co‑evolution. The authors further map dynamic‑graph learning subfields to agent capabilities, discuss potential failure modes, and outline graph‑aware evaluation and governance protocols to guide the design and oversight of self‑evolving agents.
arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2606. 16328v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows.
arXiv:2608. 10504v1 Announce Type: new Abstract: As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that systematically improves them.