AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Differentiable Hierarchical Fusion is presented, a novel framework that merges reasoning models with efficient base models via differentiable optimization to produce concise, accurate outputs and introduces a dual-factor adaptive weighting mechanism to capture intra-block variance and inter-block importance hierarchies...
Real-world inference tasks for large language models exhibit diverse difficulty levels. Existing LLM serving systems integrate models of different sizes and attempt to route tasks of appropriate difficulty to the most suitable model, aiming to reduce resource waste while guaranteeing service quality. Such systems usual...
RLCascadeRouter is a quality-estimator-free framework that formulates cascade routing as a Markov decision process with actions comprising ``stop''and model selection, and uses trajectory returns and advantages to directly optimize the performance-cost objective.
Results show that learned token-level handoffs can reduce LLM use while preserving strong reasoning performance, and show that learned token-level handoffs can reduce LLM use while preserving strong reasoning performance.
CoRun is presented, a scheduling-based system that achieves deterministic inference without requiring batch invariance, and employs isolated prefill and fixed-shape batched decode to handle the two stages of LLM inference, respectively, leveraging CUDA graphs for efficient execution and simplified implementation.
We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at a time, DiffusionGemma iteratively refines blocks of 256 tokens in parallel, avoiding the sequential decoding bottleneck of conventional au...
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