AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We study information aggregation in the networked learning model introduced by Kearns, Roth, and Ryu (SODA 2026). There is a fixed distribution over $d$ features and a common label. Agents learn in topological order on a directed acyclic graph. Each observes a subset of the features and its parents'predictions, fits a...
Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient on evolving networks where edges change over time. Existing dynamic approaches either model graph evolution through temporal GNN architectures without focusing on effici...
This work introduces *Theo*, an agentic autoformalization framework powered by general coding LLMs, and successfully formalizes their main theorems and proofs and validate the generated formalizations with human experts.
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