arXiv:2607. 26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios.
By Jingbo Cui, Jitao Zhao, Di Jin, Dongxiao He
arXiv:2609.05774v1 Announce Type: new
Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
By Katherine Tieu, Dongqi Fu, Yinglong Xia, Hong Li, Hong Yan, Jingrui He
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities,...
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...
By Yuyuan Feng, Zhishang Xiang, Chaobin Yang, Qichao Ma, Zerui Chen, Yujing Zhang, Ke Huang, Chuanjie Wu, Zhaoxu Liu, Yili Wang, Xin He, Jiapu Wang, Zijin Hong, Hao Chen, Yuanchen Bei, Kun Wang, Shengyuan Chen, Ningyu Zhang, Enyan Dai, Linhao Luo, Qingyi Pan, Qi Wang, Wenqi Fan, Guangjing Wang, Na Zou, Yangqiu Song, Xin Wang, Zechao Li, Xia Hu, Qing Li, Xiao Huang, Zhihong Zhang, Jinsong Su, Qinggang Zhang, Yi Chang
arXiv:2609.39777v1 Announce Type: new
Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...
By Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge, Qiaoyu Tan
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.
By Yuanyuan Xu, Wenjie Zhang, Yin Chen, Xuemin Lin, Ying Zhang