AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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AdaFGC is proposed, an Adaptive Federated graph clustering framework based on Global community-aware Contrastive learning that introduces an over-complete set of global community anchors to model the global community structure and adaptively estimate clustering cardinality via cross-client anchor refinement.
DiDPO is proposed, a critic-free RL method that constructs fine-grained credit units directly from the structure of code diffs directly from the structure of code diffs, offering a principled framework for fine-grained credit assignment in coding agent training.
Federated multimodal graph learning with Topology-aware Cross-modal Routing (FedTCR) is proposed, the first systematic algorithm designed for FMGL and outperforms state-of-the-art baselines on both graph-centric and modality-centric tasks.
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