AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-order adaptation can exceed the memory budget of inference-oriented deployment platforms. Zeroth-order (ZO) optimization offers a forward-only alternative with inferenc...
DASH-Q is proposed, a robust PTQ framework using diagonal Hessian approximation and iterative weighted least squares, which outperform other PTQ baselines in ultra low-bit regime and improves zero-shot accuracy by 7.01% on average and up to 14.01% over the strongest baselines.
Reported Speculative decoding (SD) speedups are difficult to compare because methods are commonly evaluated with different serving runtimes and configurations. We present SpecLLM, a pluggable evaluation framework that applies common scheduling, batching, KV-cache, CUDA Graph, and execution-backend policies across metho...
NeuRIT is proposed, a Neuron-guided Robust Instruction-Tuning framework built on a localization-first perspective that mines context-aware neurons associated with relevant and irrelevant context processing, and uses them as anchors to selectively adapt both the identified neuron groups and the layers in which they conc...
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