AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 5 of 195 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher, mitigating the distribution mismatch of off-policy supervision. While prior work has primarily trained on problems a student can largely solve, we ask whether OPD can improve a student when training is restricted to problems it cu...
Relational learning typically predicts from a database state, but decision making asks a different question: how will that state change under an action? In operational systems, the answer depends on system-specific dynamics that may not be visible in the database itself. We view each such system as a relational"world"a...
A CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance and produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.
An extensive evaluation shows that, on classification-style benchmarks, adding NLEs to few-shot prompts often improves accuracy over few-shot prompting without explanations; among NLE sources, externally generated LLM-NLEs often provide strong downstream utility and remain competitive with human rationales where both a...
A principled reward decomposition framework is introduced that decouples verifiability from sparsity, and two new reward functions are proposed: an exponential reward that provides graded penalties based on the count of forbidden-concept occurrences, and a PageRank inspired reward that weights penalties by semantic imp...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.