AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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ConvMem is a training-free, highly parallelizable framework that reformulates long-context reasoning as a hierarchical convolution, Inspired by CNNs, that outperforms training-free baselines and avoids the risk of overfitting to parametric priors often observed in RL-trained models on out-of-distribution tasks.
STAR is proposed, an efficient PbRL method that integrates preference margin regularization and policy regularization that improves feedback efficiency and facilitates more robust reward and value function learning.
HyMem is a hierarchical framework that explicitly separates the agent's context into distinct functional layers to separate high-level planning from execution and complex analysis, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.
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