AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TabFM-Auto is introduced, which pairs a tabular foundation model, TabFM, with a language model agent that evolves the data pipeline around it, and ranks first overall among MLE agents.
TabFM, a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, produces calibrated zero-shot predictions in a single forward pass without task-specific tuning.
A thorough empirical study of zero-shot multimodal forecasting approaches on TimesX suggests that many approaches that perform well on existing benchmarks may fail on TimesX, and simple ensemble methods that leverage rich textual context accompanying time-series can outperform strong baselines on TimesX.
EFE demonstrates that LLM-based evolution can improve both accuracy and interpretability when automatically tackling structured data, and is found to be particularly effective on classical decision trees.
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