AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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PaCTS is introduced, which generates a small set of instance-adaptive latent prompts in the form of continuous embedding tokens conditioned on the visible context, which serve as compact context surrogates for frozen TSFMs.
This work analyzes TSFM post-training methods based on their locus of intervention in the prediction pipeline, yielding five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization.
Tabby, a long context probabilistic time series foundation model, is released together with a complete and open recipe of how it was built, which achieves competitive zero-shot forecasting performance on GIFT-Eval and the out-of-distribution TIME benchmark.
Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.
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