AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents a general evaluation framework that enhances observability across multiple recommender systems at Netflix and demonstrates its effectiveness through several production deployments, and articulate three stakeholder-centered observability principles for content creators and model developers.
This work introduces a conditional length-penalized post-training algorithm that intertwines verification accuracy with bounded reasoning length, halting collapse and recovering performance, and offers both a scalable algorithmic patch and a long-term architectural blueprint for aligning reasoning models with real-worl...
A sequential behavioral alignment framework pairing fine-tuning with preference optimization over paired correct and counterfactual rationales is developed and applied, demonstrating that behavioral alignment mitigates bidirectional rationalization while delivering human-interpretable reasoning traces without manual pi...
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