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This paper proposes TopoMamSurv, a Graph Mamba framework for whole-slide image survival analysis that uses topology-aware ordering to address Mamba's sensitivity to input order, and incorporates bidirectional Mamba and GCN for spatial context modeling.
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that couples graph representation learning with structured, topology-constrained reasoning. It achieves absolute improvements of up to 3.44% in root cause localization accuracy and boosts fault-type classification performance by 4.39% to 16.81% across diverse datasets.