AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Dobrushin coefficient of each attention-rollout factor satisfies $\kappa(\frac12(I+A))\le\frac12(1+\kappa(A))$, and multiplying these inequalities over layers bounds the coefficient of the whole rollout. We characterise exactly when the layerwise bound is tight: equality holds if and only if some token pair attaini...
EntroPath is introduced, a manifold learning method that recovers geodesic geometry from data graphs through ensembles of diffusion paths that consistently matches or outperforms diffusion- and shortest-path-based methods, while remaining competitive with neighbourhood-preserving embeddings on local-structure metrics.
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