AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes profiling-guided layer-wise RoPE allocation, assigning high/low coverage under a fixed budget using early coverage-benefit profiles, and normalized-depth mapping transfers profiles within model families, enabling small-model measurements to guide larger-model allocations before pretraining.
A weighted Hilbert-space framework is developed and sufficient conditions under which the row-stochastic design converges faster even with a smaller spectral gap are derived, by using a Rayleigh-quotient and Loewner-order eigenvalue comparison.
ReasFlow is introduced, an end-to-end autonomous agent system for reasoning-centric scientific discovery that operationalizes a collaborative paradigm where the human expert acts as Principal Investigator while the agent executes rigorous derivations as a capable graduate student.
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