AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units to evaluate against. We address this with a Sparse Transformer (ST): a 5-layer language model trained on TinyStories whose wide, TopK-sparse MLP for...
Two cache-level interventions are introduced: Split-prefill keeps only the KV cache or only the recurrent state from a prefilled context, then generates an answer, and state-swap pairs the KV cache from one context with the recurrent state from another in a single forward pass.
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