AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work finds that coding agents are surprisingly effective at generalized TAMP: all three agent configurations outperform hand-engineered planners, one-shot generation, and an LLM-based generalized planning baseline in mean success.
ViPlan domains capture fundamental shortcomings of both VLM-grounded symbolic approaches and direct VLM planning methods, and is presented, the first open-source benchmark for comparing VLM-grounded symbolic approaches (VLM-as-grounder) with direct VLM planning methods (VLM-as-planner).
SAGE (Selective Agent Guidance via Entropy), a framework that queries a VLM only when the learner is uncertain, executes the suggested action during training, and distills guidance into a lightweight Reinforcement Learning policy, is proposed.
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