AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events, and reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.
Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the number of independently parameterized layers. We ask whether the ICL stage of such a model needs a distinct set of parameters for every layer, or only depth. We introduce T...
Across Sokoban and autoregressive FineWeb language modeling, this work finds that temporal recurrence shifts the best observed compute allocation toward substantially fewer layers, with comparable or better performance.
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