AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work reveals that the recent Muon optimizer as a mechanism that regulates this factor by construction tightens the interference bound for both CL and MM, positioning Muon as a principled optimizer-centric approach complementary to existing solutions.
This paper first proves that TFS is a sufficient condition for weight disentanglement, and finds that TFS also gives rise to an observable geometric consequence: weight vector orthogonality, which positions TFS as the common cause for both the desired functional outcome and a measurable geometric property.
Harness Continual Learning is formulated, a new continual learning paradigm in which the harness evolves around a frozen foundation model, and the resulting loss of earlier behavior as harness-level forgetting is defined.
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