AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Looped Transformers provide a parameter-efficient approach to depth scaling by repeatedly applying shared Transformer blocks. Recent reasoning models have likewise highlighted the value of scaling test-time computation through longer computation trajectories. However, the principles for designing effective loop transit...
LeapQuant is proposed, a training-free method that achieves near-lossless performance under 8-bit recurrent-state quantization and substantially reduces memory and compute costs during inference.
HyperTransfer is proposed, which constructs a Hyperball optimizer that reproduces the dynamics of a target Base Optimizer using only its initialization and learning-rate schedule, without running the target optimizer itself, to extend the framework to non-scale-invariant networks.
This work proposes Software-Interposed Datapath (SID), an efficient, software-flexible, and low-cost solution for rack-scale interconnects, particularly optimized for fine-grained memory access.
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