AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces SAGE, Semantic Audio Generative Encoder: a compact variational autoencoder that shapes its latent by distilling embeddings from a pretrained audio-text model, combining high reconstruction fidelity, state-of-the-art semantic structure, and fast inference.
It is shown that neural network weights can be explicilty fintuned to admit a smaller grammar, and the first time grammar size has been used as an explicit training objective for network weights.
Studying two independent testbeds (activation oracles and emergent-misaligned models) across three model families, it is found that the answer is threefold: merging preserves an emergent capability that both parents carry, but cannot create an emergent capability that is superadditive in its parents.
We show that a quantized model that keeps its classification accuracy still changes $14$ to $46\%$ of its top-1 retrieval results, and that aggregate ranking metrics reveal only part of this damage. We tie this failure to the gap between the two highest model scores and use that gap to decide when to trust a quantized...
This work introduces Homomorphic Encryption-Aware Training (HEAT), a fine-tuning method that makes the per-nonlinearity iteration counts learnable, enabling them and the model weights to co-adapt during training.
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