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.
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
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