The proposed AHEAD, an unsupervised anti-hallucination anomaly detection framework featuring a hallucination refinement pipeline and a temporal-structural detector, which is compatible with various T-GNN backbones for reliable anomaly detection on CTDGs.
Yingxuan Li, Yuanyuan Xu, Xue-Min Lin et al.· Proceedings of the 32nd ACM...· 0 citations
Temporal graph neural networks (T-GNNs) are powerful for representation learning on continuous-time dynamic graphs (CTDGs), but naively applying them to unsupervised anomaly detection is often unreliable. The key challenge is learning normal dynamics while being exposed to unlabeled anomalies, which destabilizes optimi...
Yingxuan Li, Yuanyuan Xu, Xuemin Lin et al.· Proceedings of the 32nd ACM...· 0 citations
This survey model 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 to provide a compact structural lens for designing and governing self-evolving agents.
Yuanyuan Xu, Wenjie Zhang, Yin Chen et al.· 2 citations
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