AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces SPatial coHErent risk control for REplay (SPHERE), a general replay-allocation method applicable across a broad range of learning settings and demonstrates that SPHERE improves accuracy and reduces forgetting across noisy-label vision tasks, continual language-model instruction tuning, and code-gen...
This work introduces Continual Adaptation through the Reuse of Validated Expertise (CARVE), a framework that represents accumulated expertise as a frozen base policy, immutable specialists, and task-specific credentials obtained through local validation that establishes expected-performance guarantees for repeated reus...
A hierarchical Multilevel Monte Carlo neural critic that performs debiasing simultaneously across trajectory sampling and critic optimization and establishes the first order-optimal convergence guarantees for infinite-horizon average-reward CMDPs with general policy parameterization and neural critics, while eliminatin...
This work proposes a hypergradient-based bilevel RL algorithm using the optimality of the Boltzmann policy for the entropy regularized discounted RL objective function and obtains an iteration complexity of $O(\epsilon^{-1})$ and state-of-the-art sample complexity of $\tilde{O}(\epsilon^{-2})$ under mild regularity con...
This work introduces Selection-Aware Semantic Stress Testing (\SASST{}), which learns a task reweighting from pre-execution features on discovery tasks and evaluates the same paired comparison on separate confirmation tasks.
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