AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We propose HiLo-RD (High-to-Low Budget Representation Distillation) to improve masked diffusion language models (MDLMs) under a limited generation budget. Unlike autoregressive models, MDLMs choose both what to generate and where to commit it. This creates two coupled challenges: predicting useful content before reason...
HappyWorld-Bench is introduced, a comprehensive benchmark that evaluates whether generated worlds remain reliable as agents interact with them, and highlights the need to evaluate world models not only by visual quality, but also by state consistency and the correctness of their responses to actions and interventions.
PReM (Preserve and Refresh Memory), a context-compression framework that maintains the long context as the model's internal layer-wise KV memory and learns what to preserve and when to refresh it, is introduced.
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